# embeddings_queue

created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2023", "link": "https://tryolabs.com/blog/top-python-libraries-2023", "language": "en", "description": "Exploring the expansive universe of Python libraries in 2023, our curated selection goes beyond the usual suspects to uncover hidden gems and innovative tools. Dive in!", "chroma:document": "\u2014 compiler that aggressively optimizes type-annotated Python code. It has several backends, including LLVM, C, C++, and WASM. LPython\u2019s primary tenet is speed. Launch blog post. Petals \u2014 Run 100B+ language models at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading.\n\n\u2014 Run 100B+ language models at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading. TokenMonster \u2014 Determine the tokens that optimally represents a dataset at any specific vocabulary size\n\nPython Programming\n\nOptimization / Math\n\nLineax \u2014 a JAX library for linear solves and linear least squares. Launch Tweet."}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2023", "link": "https://tryolabs.com/blog/top-python-libraries-2023", "language": "en", "description": "Exploring the expansive universe of Python libraries in 2023, our curated selection goes beyond the usual suspects to uncover hidden gems and innovative tools. Dive in!", "chroma:document": "Python Programming\n\nOptimization / Math\n\nLineax \u2014 a JAX library for linear solves and linear least squares. Launch Tweet.\n\n\u2014 a JAX library for linear solves and linear least squares. Launch Tweet. pyribs \u2014 a bare-bones Python library for quality diversity optimization.\n\nReinforcement Learning\n\ncheese \u2014 adaptive human in the loop evaluation of language and embedding models.\n\n\u2014 adaptive human in the loop evaluation of language and embedding models. imitation \u2014 Clean PyTorch implementations of imitation and reward learning algorithms."}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2023", "link": "https://tryolabs.com/blog/top-python-libraries-2023", "language": "en", "description": "Exploring the expansive universe of Python libraries in 2023, our curated selection goes beyond the usual suspects to uncover hidden gems and innovative tools. Dive in!", "chroma:document": "\u2014 Clean PyTorch implementations of imitation and reward learning algorithms. RL4LMs \u2014 modular RL library to fine-tune language models to human preferences. By AI2.\n\n\u2014 modular RL library to fine-tune language models to human preferences. By AI2. trlX \u2014 distributed training of language models with Reinforcement Learning via Human Feedback (RLHF).\n\nTime Series\n\naeon \u2014 A unified framework for machine learning with time series.\n\nVideo Processing\n\nVapourSynth \u2014 video processing framework with simplicity in mind. Python docs."}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: a1c2f609-a033-44c7-a1a9-d8e0426a7487
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{"title": "List of AI and Machine Learning conferences in 2024", "link": "https://tryolabs.com/blog/machine-learning-deep-learning-conferences", "language": "en", "description": "The ultimate list of the top Artificial Intelligence (AI) & Machine Learning conferences to attend in 2024.", "chroma:document": "There's a record amount of exciting Artificial Intelligence (AI) and Machine Learning (ML) conferences worldwide and keeping track of them may prove to be a challenge.\n\nThis list provides an overview with upcoming AI & ML conferences and should help you decide which one to attend, sponsor or submit talks to.\n\nFor some conferences we added remarkable speakers and discount codes. Those with the tag \"Tryolabs' Pick\" indicate our personal favorites. Drop us a line here if you think of a conference that should be added to the list.\n\nWant to be notified when new conferences are added to this list? Sign up and the monthly updates will be delivered right to your inbox.\n\nLast update: April 2024"}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: effcf37e-5893-4f99-828f-8de8f74d23f2
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{"title": "List of AI and Machine Learning conferences in 2024", "link": "https://tryolabs.com/blog/machine-learning-deep-learning-conferences", "language": "en", "description": "The ultimate list of the top Artificial Intelligence (AI) & Machine Learning conferences to attend in 2024.", "chroma:document": "Last update: April 2024\n\nCategories\n\nContinent\n\nThinking of implementing a machine learning project in your organization? We have compiled 11 essential questions to ask before kicking off a ML initiative. Go to the list"}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: e1093ae2-ef55-4a31-a6df-1298b0459723
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": "Welcome to a journey through the heart of Latin America's AI innovation, captured at the Impulse Data & AI Conference held in Mexico City. This two-day event not only served as a confluence of minds eager to decode the future of artificial intelligence but also as a testament to the region's growing influence on the global AI stage. As both a speaker and an attendee, I had the privilege to immerse myself in the groundbreaking discussions and innovations that unfolded. Through this post, I aim to share the knowledge and insights gained, offering you a window into the future of AI as envisioned by some of the field's most forward-thinking pioneers.\n\nKey themes and innovations"}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": "Key themes and innovations\n\nThe conference was a kaleidoscope of AI's vast potential, with themes ranging from the technical intricacies of machine learning models to the ethical considerations of AI applications. Among the standout topics were the transformative potential of large language models (LLMs), the imperative of ethical AI use, and the strategic importance of MLOps practices in realizing AI's full potential. These discussions underscored a collective pursuit to not only advance AI technology but to do so with a keen awareness of its societal impact.\n\nInsights from notable sessions"}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": "Insights from notable sessions\n\nThe heart of the conference lay in its rich agenda of talks, where industry leaders shared their expertise and visions for the future of AI. Here are highlights from some of the sessions that resonated most deeply with me:\n\nThe power of embeddings : James A. Rolfsen from Rappi illuminated the pivotal role of embedding engineering in enhancing search and personalization. His insights into the profitability and technical strategies behind embeddings offered a compelling look at how AI can tailor digital experiences to individual user needs."}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": ": James A. Rolfsen from Rappi illuminated the pivotal role of embedding engineering in enhancing search and personalization. His insights into the profitability and technical strategies behind embeddings offered a compelling look at how AI can tailor digital experiences to individual user needs. Graph Neural Networks (GNNs) unleashed : Jaime D. Acevedo's exploration of GNNs and their application in fraud detection, credit risk estimation, and recommendation systems provided a fascinating glimpse into how AI can add layers of context to data. The real-world examples from companies like Snapchat and Amazon highlighted the practical value of these models in solving complex business problems."}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": ": Jaime D. Acevedo's exploration of GNNs and their application in fraud detection, credit risk estimation, and recommendation systems provided a fascinating glimpse into how AI can add layers of context to data. The real-world examples from companies like Snapchat and Amazon highlighted the practical value of these models in solving complex business problems. A new lens on data: Christian Ramirez introduced the audience to Topological Data Analysis (TDA), an emerging field that promises to revolutionize how we approach high-dimensional data sets. His presentation on using TDA for anomaly detection and problem-solving in machine learning models underscored the innovative ways in which AI can"}


created_at: 2024-05-13 19:07:57
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": "His presentation on using TDA for anomaly detection and problem-solving in machine learning models underscored the innovative ways in which AI can enhance our understanding of data."}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": "Each of these sessions not only deepened our understanding of AI's technical frontiers but also sparked conversations about its practical applications across industries.\n\nCase studies and real-world applications\n\nThe conference wasn't just about theoretical knowledge; it was also a showcase for AI's real-world impact:\n\nAI in the sky : Nicol\u00e1s Venegas' discussion on integrating AI within LATAM Airlines revealed how technology is reshaping the airline industry. From optimizing fuel efficiency to revolutionizing revenue management and cargo optimization, his presentation underscored AI's role in enhancing operational efficiency and reducing costs."}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 350a734b-09bf-4768-8e2b-73658bd5d79c
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": ": Nicol\u00e1s Venegas' discussion on integrating AI within LATAM Airlines revealed how technology is reshaping the airline industry. From optimizing fuel efficiency to revolutionizing revenue management and cargo optimization, his presentation underscored AI's role in enhancing operational efficiency and reducing costs. Visionary healthcare: E. Ulises Moya shared an inspiring case study from Jalisco, where AI systems are being used for patient referral with diabetic retinopathy. This initiative, recognized by the Global Partnership on AI (GPAI), showcased how AI could offer life-changing benefits in healthcare by improving diagnosis and treatment processes.\n\nThe MLOps imperative"}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": "The MLOps imperative\n\nAs AI continues to evolve at a breakneck pace, the conversation around effectively integrating AI into real-world applications has never been more critical. This event illuminated this crucial dialogue through a focused exploration of MLOps, and it was brought to life through practical insights.\n\nNavigating the MLOps landscape at Nissan\n\nRodrigo Olivares opened the discussion with a candid look into the MLOps challenges encountered at Nissan. His talk underscored the critical nature of MLOps in transforming AI models from experimental prototypes into robust, scalable solutions that drive business value. Key highlights from his session included:"}


created_at: 2024-05-13 19:07:57
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": "Operational challenges : Olivares shared the real-world difficulties of deploying AI models without a structured MLOps framework, leading to bottlenecks and inefficiencies that hindered timely implementation.\n\n: Olivares shared the real-world difficulties of deploying AI models without a structured MLOps framework, leading to bottlenecks and inefficiencies that hindered timely implementation. Collaborative solutions : The necessity for a collaborative ecosystem was emphasized, bridging the gap between data scientists, engineers, and IT staff to foster a productive environment for AI deployment."}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": ": The necessity for a collaborative ecosystem was emphasized, bridging the gap between data scientists, engineers, and IT staff to foster a productive environment for AI deployment. Path to success: Offering a beacon of hope, Olivares discussed strategies that Nissan employed to overcome these obstacles, focusing on building a supportive MLOps culture and investing in continuous learning and development.\n\nThe MLOps journey unveiled: Tryolabs roadmap to operational excellence"}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": "The MLOps journey unveiled: Tryolabs roadmap to operational excellence\n\nMy presentation aimed to provide a structured pathway through the complexities of MLOps, drawing from my experience in the field. The \"Tryo-way\" roadmap I introduced is designed to guide teams from the initial stages of data preparation to the continuous monitoring and improvement of deployed models.\n\nHere's an overview of the journey:"}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": "Data organization: Highlighting the cornerstone of any successful AI project, I delved into effective data management practices essential for laying a solid foundation. Model development and tracking: I discussed the significance of establishing rigorous processes for model experimentation and tracking, ensuring transparency and reproducibility throughout the model lifecycle. Streamlining production: The transition from experimental models to production environments was explored, with a focus on standardization and automation to facilitate scalability and maintainability. Continuous monitoring and iteration: Lastly, the importance of ongoing vigilance in monitoring model performance and"}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": "scalability and maintainability. Continuous monitoring and iteration: Lastly, the importance of ongoing vigilance in monitoring model performance and adapting to new data or changing conditions was emphasized, ensuring that AI solutions remain effective and relevant over time."}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": "For those looking to explore each stage of the MLOps journey in greater depth, I highly recommend our previous blog post, where we dissect the nuances and strategies pivotal to mastering MLOps.\n\nGenerative AI: opportunities, challenges, and horizons\n\nThe conference convened a distinguished panel of experts in Generative AI, offering profound insights into its capabilities, ethical considerations, and the trajectory of future developments. Here's an enhanced perspective on the discussions that unfolded:"}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": "Sasha Luccioni's session, \"Generative AI Models: The Good, the Bad, and the Bias,\" served as a cornerstone for understanding the dual-edged nature of generative AI. She adeptly highlighted the transformative applications of technologies like ChatGPT and Dall-E 2 in enhancing user interactions through search and customer service. Yet, Luccioni conscientiously r aised the flag of caution , urging a thoughtful consideration of generative AI's limitations. Her presentation was a clarion call for the adoption of human-centered, ethical frameworks in AI development, emphasizing the importance of navigating the innovation landscape responsibly ."}


created_at: 2024-05-13 19:07:57
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": ", urging a thoughtful consideration of generative AI's limitations. Her presentation was a clarion call for the adoption of human-centered, ethical frameworks in AI development, emphasizing the importance of . Mabel Ger\u00f3nimo shifted the narrative towards the practical implications of Generative AI in the realm of software development, focusing on GitHub's AI assistant, Copilot. By unveiling the underestimated benefits of generative AI in coding\u2014ranging from heightened productivity to expedited deployment cycles\u2014she painted a picture of a future where AI tools become indispensable allies in the development process . Her foresight into the evolution of AI-assisted tools offered a roadmap for"}


created_at: 2024-05-13 19:07:57
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": "where AI tools become indispensable allies in the development process . Her foresight into the evolution of AI-assisted tools offered a roadmap for staying at the cutting edge of technological advancement."}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": ". Her foresight into the evolution of AI-assisted tools offered a roadmap for staying at the cutting edge of technological advancement. Daniel Vaughan, bringing two decades of experience and authorship of pivotal O'Reilly publications, delved into the nuanced impact of Generative AI on the data science discipline. Highlighting the irreplaceable value of human skills such as analytical thinking and causal reasoning, Vaughan's discussion was a poignant reminder that despite AI's advances, certain human capabilities remain uniquely ours. His vision for the future of data science education\u2014emphasizing critical thinking over mere programming prowess\u2014suggests a paradigm shift towards nurturing"}


created_at: 2024-05-13 19:07:57
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": "for the future of data science education\u2014emphasizing critical thinking over mere programming prowess\u2014suggests a paradigm shift towards nurturing skills that AI cannot replicate."}


created_at: 2024-05-13 19:07:57
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": "These discussions underscored a collective recognition of Generative AI's immense potential to redefine industries and human interactions. Yet, they also underscored the imperative for ethical vigilance and the cultivation of uniquely human skills that complement AI's capabilities. As we stand on the precipice of this new technological era, these insights serve as both a guide and a warning\u2014prompting us to forge ahead with innovation, yet always with an ethical compass in\n\nWrapping-up"}


created_at: 2024-05-13 19:07:57
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": "Wrapping-up\n\nThe event was packed with technical talks, networking opportunities, and insightful discussions. As a speaker, I was able to share my experiences and insights, and as an attendee, I was able to learn from some of the great minds in the field. The diversity of topics covered, from the latest AI trends to practical data science applications, made the event a rich learning experience for everyone involved."}


created_at: 2024-05-13 19:07:57
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{"title": "Unpacking the Impulse Data & AI Conference 2024", "link": "https://tryolabs.com/blog/unpacking-the-impulse-data-&-ai-conference-2024", "language": "en", "description": "Dive into AI innovation at the Impulse Data & AI Conference 2024 in Mexico City. Discover how Latin America is shaping the global AI landscape through groundbreaking discussions and real-world applications, from embedding engineering to ethical AI use and MLOps.", "chroma:document": "Impulse Data & AI 2024 was more than just a conference; it was a celebration of the AI and data science community. It was a place where professionals, enthusiasts, and the general public could come together to learn, share, and inspire each other. Despite my initial nerves, I left the event feeling more connected to my field and more excited about the future of AI and data science."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "The quest for efficient and user-friendly solutions has led to the emergence of a game-changing concept: Automated Machine Learning (AutoML). AutoML is the process of automating the tasks involved in the entire Machine Learning lifecycle, such as data preprocessing, feature engineering, model selection, and hyperparameter optimization. It aims to make Machine Learning easier to understand, faster to prototype, and more effective, for a broader range of people, including experienced data scientists but also professionals with limited knowledge in the field."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "In this blog post, we will explore the dynamic world of AutoML, highlighting its advantages, as well as the challenges and limitations it presents. Moreover, we will delve into an exploration and comparison of the most notable tools in this domain."}


created_at: 2024-05-14 14:30:36
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "For the sake of comparison, we will use Computer Vision tasks to guide our evaluation. This will provide a concrete, illustrative example through which we can compare and evaluate the capabilities of different AutoML providers. This decision, however, should not be seen as limiting the scope of our analysis strictly to the realm of Computer Vision. Instead, it acts as a lens, offering clarity and insight into the broader landscape of AutoML tools.\n\nAdvantages of AutoML"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "Advantages of AutoML\n\nAutoML covers every section of the end-to-end Machine Learning pipeline, from data preparation and feature engineering to model selection, hyperparameter tuning, performance evaluation, and even some aspects of model deployment. By automating these complex tasks, more people and businesses can gain access to this technology without understanding the underlying algorithms.\n\nLow entry barrier for non-experts"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "Low entry barrier for non-experts\n\nBuilding on this democratization effort, AutoML's user-friendly approach empowers individuals from diverse backgrounds, such as business analysts, domain experts, and decision-makers. AutoML removes the need for in-depth knowledge of algorithms, coding languages, or intricate model tuning and the collaboration flourishes, opening up new avenues for innovation in industries where data-driven decision-making was once limited to a select few.\n\nEnables businesses without dedicated Data Science teams"}


created_at: 2024-05-14 14:30:36
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "Enables businesses without dedicated Data Science teams\n\nWhile we don't recommend entirely disposing of a Data Science team, this can benefit particularly small businesses, where establishing a dedicated team presents resource challenges. AutoML becomes a game-changer, enabling businesses to harness Machine Learning capabilities with the reduced necessity for a specialized team. This reduces costs and accelerates the integration of Machine Learning into everyday operations."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "AutoML proves to be essential not only for beginners but also for advanced data scientists. Even experts can benefit from its time-saving capabilities, allowing them to focus on strategic aspects like problem framing, domain understanding, and interpreting model outputs.\n\nSpeeding up model development"}


created_at: 2024-05-14 14:30:36
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "The time-intensive nature of manual Machine Learning tasks often hinders models\u2019 rapid development and deployment. AutoML addresses this challenge by expediting model development through quick prototyping and experimentation. Automation enables the exploration of various algorithms and configurations, facilitating the identification of optimal models in a fraction of the time it would take through traditional methods. The combination of automation and ensemble also enhances the adaptability to complex relationships within the data that are challenging for a single model to capture comprehensively, even in some cases yielding better performance than any single model on its own."}


created_at: 2024-05-14 14:30:36
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "Efficient use of time AutoML platforms efficiently navigate the vast search space of potential models and configurations, allowing data scientists and practitioners to focus on higher-level tasks, such as interpreting results and refining strategies. This aspect enhances the overall efficiency of the Machine Learning pipeline.\n\nChallenges and limitations of AutoML\n\nWhile AutoML brings forth a paradigm shift in democratizing Machine Learning, it is crucial to acknowledge and address the challenges that accompany this transformative technology.\n\nHandling domain-specific knowledge"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "Although it's designed to be user-friendly, AutoML tools may struggle to incorporate domain-specific knowledge effectively. Understanding the intricacies of a specific industry or problem often requires human intuition and expertise. Integrating domain-specific insights into the automated processes of data preprocessing, feature engineering and model selection, remains challenging, as it involves capturing nuanced patterns and context that may be beyond the scope of automated algorithms. In our experience, the most accurate models combine hand-coded domain-specific features derived from expert knowledge in the field with automated model exploration, parameterization, and ensembling. It\u2019s a"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "domain-specific features derived from expert knowledge in the field with automated model exploration, parameterization, and ensembling. It\u2019s a perfect case of human-machine collaboration."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "Limited customization\n\nAutoML platforms often prioritize simplicity and generalization to cater to a broad audience. However, this emphasis on ease of use may limit the degree of customization available to advanced users. Tailoring models to specific needs or integrating proprietary algorithms can be challenging within the constraints of some AutoML frameworks, potentially restricting their adoption in specialized domains.\n\nInterpretability and explainability"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "Interpretability and explainability\n\nAs AutoML systems become more sophisticated, the interpretability and explainability of the generated models become critical concerns. Understanding why a particular model makes a specific prediction is essential, especially in domains where decisions have significant consequences. The inherent complexity of some automated models may make it challenging to provide clear explanations, raising ethical and practical concerns in applications where transparency is crucial and, in many cases, requiring additional tools to deduce what may be going on inside the black box."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "There are also challenges and limitations associated with efficiently using resources. The extreme simplification of the development process achieved with AutoML often results in over-complex and heavier-to-maintain model architectures.\n\nTraining and prediction time\n\nAutoML might still require a considerable amount of computational resources and time. In most cases, manual model development with carefully chosen configurations will outperform or match the performance of automated solutions in reduced training time."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "Some highly complex models generated by AutoML processes may have longer prediction times, especially if they involve deep neural networks. It's essential to strike a balance between model complexity and deployment efficiency, considering applications\u2019 specific needs.\n\nResource intensive\n\nCertain models selected by AutoML may be resource-intensive, particularly on RAM/VRAM and computationally expensive during training and prediction phases. It's important to assess whether the chosen model is aligned with the available computational resources and meets the necessary criteria for prediction time."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "Costs might be another challenge since training and running AutoML models tend to be more expensive than simpler models. In the long run, this could be an issue due to the scaling and accumulated cost of the resources over time."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "Choosing the right AutoML tool for your specific needs becomes increasingly crucial. This comprehensive dimensional analysis delves into key aspects that can guide your evaluation of AutoML tools. Each dimension provides valuable insights into the capabilities and limitations of the tools, enabling you to make an informed decision tailored to your requirements. Although other factors, such as accuracy or overall performance, could also be considered, they require a deeper analysis for a future review."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "As stated at the beginning of this blog post, Computer Vision will serve as our comparative framework. This choice allows us to systematically analyze and highlight the strengths and limitations of different AutoML providers. The findings, primarily grounded in the field of Computer Vision, are meant to guide choices across a range of potential use cases, providing a broad perspective on selecting and utilizing AutoML tools in various scenarios."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "Dimension Key aspects Support for vision tasks Evaluate range of supported tasks like segmentation, pose estimation, and identify limitations. Customization Examine flexibility in providing custom code and declarative changes to training behavior. Ease of use Analyzing how accessible the tool can be for users who do not have high expertise in Machine Learning model development. Cost Analyze the cost associated with the utilization of each tool. Data management Evaluate capabilities for labeling, collaboration, assistance mechanisms, and EDA support. Production readiness Assess the ability to export model files, create production-ready endpoints, and avoid vendor lock-in. MLOps readiness"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "support. Production readiness Assess the ability to export model files, create production-ready endpoints, and avoid vendor lock-in. MLOps readiness Explore the capability to create end-to-end pipelines, monitor performance, and support continuous training. Community and support Consider active community presence, availability of official support, and developers' background. Documentation Evaluate the comprehensiveness and clarity of available documentation."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "Below, you can see the comparative ratings for the different tools. As you can see, a star-based scale was used, where a higher number of stars indicates a better comparative performance in that dimension.\n\nDid we miss a standout tool or feature? Don't be shy - drop us a message with your recommendations!\n\nAutoGluon is a free and open-source Python library that can handle diverse datasets, including images, text, and tabular data. With an emphasis on simplicity, Autogluon delivers integrated support for automatic deployment to the Amazon Web Services (AWS) ecosystem while operating in a cloud context, along with a smooth local execution experience."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "When it comes to flexibility and customization, AutoGluon is an excellent choice. This tool supports a wide range of vision tasks, including object detection, image classification, image-image matching, image-text semantic matching and semantic segmentation. Additionally, AutoGluon's hyperparameter tuning capabilities, along with its data augmentation settings, allow users to fine-tune their models for optimal performance."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "However, there are also limitations to consider. AutoGluon lacks specific features related to labeling and data analytics. Additionally, MLOps functionalities, such as monitoring or model tracking, are not covered. Users may need to rely on other tools to fulfill these needs.\n\nFurthermore, it is important to note that AutoGluon operates as a Python library, which requires some coding skills. This characteristic makes it less codeless compared to other alternatives in the market."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "As part of Google Cloud's Machine Llearning platform, Vertex AI AutoML stands out for its user-friendly approach, allowing users to train different types of data for Machine Learning applications without requiring coding and using a carefully developed interface to make it intuitive and easy to use.\n\nA major strength of Vertex AI AutoML is its seamless integration with Google Cloud's infrastructure, optimizing Machine Learning workflows for efficiency and scalability. Deploying models to production within the Google environment is streamlined, and the platform offers an array of tools for MLOps, including model versioning and monitoring pipelines for continuous integration."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "While Vertex AI AutoML provides a range of vision tasks, including object detection and classification for images, as well as video data tasks like action recognition, classification, and object tracking, it does have certain limitations. Users may find that their use cases are somewhat restricted, leading them to explore alternative solutions for greater flexibility in model development."}


created_at: 2024-05-14 14:30:36
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "An important final consideration is that the platform relies heavily on the Google environment, which could be a limitation if your infrastructure is on another cloud provider, or you don't want to depend on a specific cloud provider to develop and deploy your Machine Learning service. This service runs on the cloud; therefore, it is important to thoughtfully consider the associated costs. The pricing structure depends on the model type and running time.\n\nDriverless AI builds upon the strengths of the H2O.ai open-source platform, offering a simplified development experience through its user-friendly interface."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "This tool has a codeless interface and offers high levels of customization and adaptability. Users can choose to use Python for further customization and it provides a variety of configuration options to fine-tune models. Furthermore, the tool includes data augmentation capabilities and a comprehensive explainability toolkit. These features are highly valuable for understanding and improving the quality of the model."}


created_at: 2024-05-14 14:30:36
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "From the perspective of limitations, it primarily caters to classification and regression tasks and lacks extensive support for vision-related functionalities. Moreover, it falls short in areas such as continuous training, model versioning, and monitoring within a production environment.\n\nBeing an integral part of the Azure platform, Azure AutoML simplifies the transition from model development to production, offering seamless integration with MLOps practices. Tasks such as putting models into production, automatic retraining, and monitoring are streamlined within the Azure ecosystem."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "One of the notable advantages of Azure AutoML is its flexibility in catering to both code-first and no-code experiences. This allows users to choose an approach that aligns with their preferences and expertise.\n\nThe tool has extensive capabilities, especially in the field of Computer Vision tasks. It supports multi-class image classification, multi-label image classification, object detection, and instance segmentation. Additionally, the platform provides customization features that enable users to customize the search space of model hyperparameters, including options such as model architectures, learning rates, optimizers, and more options."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "However, there are certain limitations to consider. While Azure AutoML supports data augmentation for Computer Vision tasks, users have limited control over the specific transformations applied. The platform applies predefined transformations based on the task, with minimal flexibility for users to fine-tune or customize these augmentations according to their specific requirements.\n\nFinally, it is important to consider the cost associated with each use case and the potential reliance on the Azure environment that may come with this proprietary solution built on the Azure cloud."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "One of the primary strengths of Roboflow is its specialization in Computer Vision, offering comprehensive functionalities for annotation and preprocessing. The platform's user-friendly interface makes data augmentation a straightforward process, allowing users to effortlessly select and apply transformations with a simple click. Roboflow supports a wide range of Computer Vision tasks, including object detection, classification, instance segmentation, semantic segmentation, and keypoint detection."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "Notably, Roboflow stands out for its ease of use, providing a Python API and a web interface for users to create datasets, upload data, modify annotations, split datasets, and add preprocessing and augmentation transformations with minimal effort. During the training process, users can monitor metrics such as loss and accuracy in real time, estimate training times, and receive email notifications upon job completion."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 093e9ebe-8df3-43b8-a689-cca9f0470dba
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "However, users should be aware of the associated costs, as while free plans are available, there are also paid plans with additional features. As of the information available, Roboflow lacks features related to continuous training, model monitoring, and model versioning, which may be essential for certain use cases and workflows."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "Ludwig is an open-source Machine Learning platform that primarily focuses on LLMs (Large Language Models), but it also covers other domains like Computer Vision and predictive modeling. Ludwig stands out with its key features of being low code, declarative, and highly customizable. It is designed to facilitate Machine Learning tasks with minimal coding, providing a user-friendly experience while offering significant flexibility in customization."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "Ludwig presents several advantages for users. It supports a variety of data augmentation transformations, such as horizontal and vertical flip, rotation, blur, brightness, and contrast. Users enjoy complete freedom in selecting and applying these transformations based on their specific needs. The platform's high degree of customization extends beyond data augmentation, allowing users to set hyperparameters for optimization and define search spaces for hyperparameter tuning.\n\nHowever, it is important to remember that while low-code, the tool still requires a basic understanding of programming to be used effectively."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "However, it is important to remember that while low-code, the tool still requires a basic understanding of programming to be used effectively.\n\nUnlike some other platforms, Ludwig does not provide labeling solutions. Users are required to handle this aspect separately. Additionally, Ludwig does not have built-in functionalities for model versioning, continuous training, or monitoring of deployed models. Users may need to supplement Ludwig with additional tools or practices to address these aspects of the Machine Learning lifecycle."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 848bcd5f-cf28-409e-811d-7a6deb78055a
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "Amazon SageMaker Canvas simplifies Machine Learning for users with limited coding skills by offering a no-code interface and exploratory data analysis (EDA) features. These capabilities allow users to visually explore data before model building, leading to informed decisions about feature engineering and model selection."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 0038c527-0ee7-4859-82bd-ba184e660a6b
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "A key strength of the platform, particularly for AWS infrastructure users, is its seamless integration with the AWS environment. This facilitates easy model deployment and leverages the scalability and reliability of AWS services. Furthermore, SageMaker Canvas extends beyond the modeling stage, offering MLOps tools for labeling, reproducibility, automation, and monitoring."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 6bf0a181-76a0-4c7b-a7e5-1845f08f79fa
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "However, it's important to note that SageMaker Canvas may not be the best choice for complex Computer Vision tasks. The platform only offers image classification functionalities, which may not be sufficient for a wide range of Computer Vision scenarios. Additionally, the expected cost of this service should be considered depending on the intended use case.\n\nFinal thoughts"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: ed9c6da0-cc04-4268-a2ca-80734f36ac0c
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "Final thoughts\n\nIn conclusion, the emergence of AutoML represents a significant advancement in the accessibility of Machine Learning, allowing a wider audience to leverage the potential of AI. However, it is important to acknowledge that while AutoML is a valuable tool, it is not a one-size-fits-all solution, and its effectiveness varies depending on the specific use case.\n\nFurthermore, the market offers a wide variety of tools that cater to different needs. In the Computer Vision domain, industry leaders such as Vertex AI, Azure AutoML, and Sage Maker Canvas provide platform-like solutions prioritizing seamless integration and easy deployment."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "On the other hand, more specialized tools like H2O and Roboflow offer user-friendly interfaces without being tied to a specific cloud service provider. Additionally, free and open-source low-code tools like Ludwig and AutoGluon are available, allowing users to engage in model development with simplicity and customization."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: e03e8f8b-1064-489b-8b7c-856b050bee9a
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metadata:
{"title": "AutoML decoded: the ultimate guide and tools comparison", "link": "https://tryolabs.com/blog/automl-decoded-guide-and-tools-comparison", "language": "en", "description": "Unlock the full potential of Automated Machine Learning with our ultimate guide. Dive into AutoML's core concepts, advantages, and challenges, and explore a detailed comparison of leading tools.", "chroma:document": "Ultimately, it's about selecting the tool that best matches your needs to fully embrace the extensive benefits AutoML offers. If this discussion has sparked your interest and you're curious about how AutoML can be tailored to the unique needs of your organization or business, we're here to help. Reach out to us to discover how these cutting-edge technologies can drive innovation, efficiency, and growth in your projects."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: a5d9dd6c-b251-4c32-aff0-21640dcb87d7
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "Data is the new oil, fueling the AI revolution. From user-tailored shopping assistants to AI researchers, to recreating the King, the applicability of AI models knows no bounds. Yet these models are only as good as the data we feed them.\n\nState-of-the-art generative AI models offer the potential to automate information extraction processes, providing a unified and adaptable approach that can ingest vast amounts of raw data in a short timespan. However, integrating a large language model into a production pipeline can be both costly and challenging."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 21d6145e-2d68-4f95-a4db-d9b4e97aa1f3
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "In this blog post, we'll explore an effective way to leverage a powerful model like GPT-4 to produce titles for unstructured text, and then fine-tune your own open-source LLM with Predibase to perform accurate, controlled, and cost-effective inference. This task falls under the umbrella of automatic summarization (i.e., computationally extracting a small subset of information, representative of a larger document), but these ideas are applicable to the whole category of information extraction problems. So, if you're interested in unlocking the full potential of your data, keep on reading.\n\nThe power of Large Language Models (LLMs)"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 093de914-6ce2-4613-ad09-e528d5eec196
encoding: FLOAT32
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "The power of Large Language Models (LLMs)\n\nLLMs are AI models that have been trained on vast amounts of data, enabling them to both digest and produce human-like text. Their ability to understand and generate text makes them perfect for the task of information extraction. Instead of having teams of manual laborers perform a mechanical and low-engagement task, a single data scientist can interact (\u201dprompt\u201d) with the LLM, asking it to produce the required information. Inspecting the produced information, the scientist can apply techniques such as prompt-engineering and retrieval-augmented generation (RAG) to refine the model\u2019s results, further improving the quality of automated results."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 24e85345-aeda-4da8-95c5-7d78d9207d58
encoding: FLOAT32
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "These tasks can take the form of automatic summarization, but also of any other Natural Language Processing task such as sentiment analysis, classification and the wider category of information extraction in general. With the advent of Multimodal LLMs, source information doesn\u2019t even need to be text, images, audio and any kind of digitized data can be processed.\n\nWe now have the information extractor that we need. We can\u2019t quite call it a day yet though. While state-of-the-art LLMs such as GPT-4 are incredibly powerful, their size and complexity make them expensive to use, reducing their applicability on projects with high query volumes.\n\nThe strategy: training cheaper models with LLMs"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 609f70ca-5a5a-4c71-abfa-38c6fe486686
encoding: FLOAT32
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "The strategy: training cheaper models with LLMs\n\nThe idea is simple yet powerful: use an expensive LLM to perform an initial, high-quality and zero-shot, low-volume first pass, then use this data as labels to fine-tune a cheaper LLM, that instead of being a generalist like GPT-4 focuses only on your specific problem."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 2ada5ab0-4873-4255-ba0e-ca299b1fef9c
encoding: FLOAT32
metadata:
{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "This approach gives you the best of both worlds. You get the high-quality data labeling capabilities of an expensive LLM on your specific task, while also reaping much lower ongoing costs because of the smaller, more specific and cheaper model for your actual business operations. Inference costs of smaller models is orders of magnitude smaller than those of proprietary API calls, with typical cost reductions in the 40X-200X range.\n\nAnother advantage of fine-tuning is that you won\u2019t need as specific prompts, leading to needing fewer input tokens and therefore lower cost."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 327b4079-b8c1-4a4c-bdc5-6df187e5ac6d
encoding: FLOAT32
metadata:
{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "A third, often neglected advantage is that these models are Open Source. You can export the weights of your finetuned model and use readily available Open Source libraries, like llama.cpp or vLLM, to serve them where you see fit. This allows for secure prompting without the need for sending your private data to third parties and also to design your own scaling solutions avoiding rate limits and ensuring required throughputs. Also, LLM API providers will make changes to their underlying models and possibly deprecate API versions, by self hosting your own language model this risk is avoided entirely."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: cf139b38-b019-4e99-acfc-c6c9955367ed
encoding: FLOAT32
metadata:
{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "This strategy is cost-effective, scalable, private, and achieves high-quality outputs, making it an ideal solution for businesses of all sizes.\n\nCase study: leveraging GPT-4 for fine-tuning an inexpensive LLM for AI news summarization\n\nIn this section we\u2019ll show a real life case study on how the problem of information extraction can be resolved in a high quality cost effective fashion.\n\nThe task we\u2019ll solve is creating an AI agent capable of understanding News articles related to AI and producing meaningful titles for them. This falls under the umbrella of Automatic Summarization.\n\nThe dataset"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: bb8de003-af06-4b38-989e-5b48ff99445e
encoding: FLOAT32
metadata:
{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "The dataset\n\nFor this, we compiled a database from the \"AI News Shorts\" section of MarkTechPost, a reputable source for current AI trends.\n\nAI News database.\n\nThis dataset, consisting of numerous articles, posed a significant challenge for labeling, due to its sheer volume and the dense, technical nature of the content.\n\nPutting generative AI into action: by using GPT-4 Turbo, specifically the gpt-4-1106-preview model, we generated titles using this customized prompt:"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 984aff73-3bf7-4015-8c6f-0ff499a557b0
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "You are a highly skilled marketing AI consultant trained in language comprehension, summarization and bizdev. You have to read the following AI news text and extract a concise, catchy title, no more than 60 characters long. Include in the title the name of the development and why it's relevant: this may be because of technological advancements, societal impact, business applications, investment opportunities, security and privacy, AI ethics, because the author is well known (ie Google, Meta, Apple, etc), or other factors. These are some good example titles: Code Llama, an Open-Source LLM for coding\n\nDeciCoder, a 1B Params FOSS Code-Gen LLM by Deci\n\nGoogle Unveils Multimodal Gemini\" AI"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "DeciCoder, a 1B Params FOSS Code-Gen LLM by Deci\n\nGoogle Unveils Multimodal Gemini\" AI\n\nTII released Falcon 180B\n\nMicrosoft unveils custom AI chips\n\nSynthID, identifying AI-generated images\n\nMeta advances the development of social embodied AI agents\n\nReplit now offers free access to AI Assistant\n\nMilestone in AI breast cancer detection\n\nMicrosoft brings Python to Excel\n\nGraphCast, faster, more accurate, global weather forecasting\n\nNExT-GPT, An any-to-any Multimodal LLM\n\nGrok, a Hitchhiker's Guide to the Galaxy based AI, by xAI\n\nQ-ALIGN, Aligning AI Image Assessment with Human Judgment"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "Grok, a Hitchhiker's Guide to the Galaxy based AI, by xAI\n\nQ-ALIGN, Aligning AI Image Assessment with Human Judgment\n\nLLMs Crack Visual Code, MIT CSAIL's Text-To-Image Synthesis Include only the summarized title in the response. AI News Text: {text}\n\nThe approach we just took is an application of synthetic data generation, where we had an AI model create the information for us.\n\nFor the sake of this example, we used this prompt to generate 400 titles that we manually curated one by one. During this process we found 59% of the generated titles needed no corrections while the remaining 41% needed some form of adjustment."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "We took note of the \u201cerrors\u201d the model made (in this context we wanted the model to generate user-friendly titles so we considered specialized acronyms and jargon as errors) and found titles like these:\n\nFlowVid: Boosting V2V Synthesis Efficiency & Quality DL3DV-10K: Pioneering NVS with Real-World Data MoE LLMs Optimized for Consumer Hardware\n\nReaders familiar with AI jargon may realize that V2V refers to Video-2-Video, NVS to Neural View Synthesis and MoE to Mixture of Experts, but for newcomers these acronyms may be difficult to read.\n\nWe also found the model had some issues identifying two kinds of news articles: tool compilations and dataset releases."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "We also found the model had some issues identifying two kinds of news articles: tool compilations and dataset releases.\n\nFor the tool compilations, things such as \u201cTop 40 Generative AI tools (December 2023)\u201d, GPT-4 produced a title related to the first tool on the list, instead of realizing the article was a compilation. For the linked article, the first title GPT-4 produced was this:\n\nMultimodal GPT-4 Elevates AI Capabilities & Safety"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "Multimodal GPT-4 Elevates AI Capabilities & Safety\n\nIn the case of dataset releases, such as a paper introducing DL3DV-10K\u2014a large-scale scene dataset for 3D Vision\u2014the main interest lies in the new data's availability for benchmarking aspects of AI. However, the titles generated often missed the mark, focusing incorrectly, as seen here:\n\nDL3DV-10K: Pioneering NVS with Real-World Data\n\nHaving identified these issues we reformulated the prompt to this (modifications highlighted in bold italic):"}


created_at: 2024-05-14 14:30:36
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "You are a highly skilled marketing AI consultant trained in language comprehension, summarization and bizdev. You have to read the following AI news text and extract a concise, catchy title, no more than 60 characters long. Avoid overly specialized tech jargon and little known acronyms, but use buzzwords and keywords related to the area of application. Include in the title the name of the development and why it's relevant: this may be because of technological advancements, societal impact, business applications, investment opportunities, security and privacy, AI ethics, because the author is well known (ie Google, Meta, Apple, etc), or other factors. These are some good example titles: Code"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "and privacy, AI ethics, because the author is well known (ie Google, Meta, Apple, etc), or other factors. These are some good example titles: Code Llama, an Open-Source LLM for coding"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "Grok, a Hitchhiker's Guide to the Galaxy based AI, by xAI\n\nQ-ALIGN, Aligning AI Image Assessment with Human Judgment\n\nLLMs Crack Visual Code, MIT CSAIL's Text-To-Image Synthesis Include only the summarized title in the response. Don't enclose your title in quotes. If the article refers to the release of a dataset, identify the dataset's relevance. If the article isn't a news article but instead a compilation of developments, such as: 40 AI code tools you can try today, identify it and produce an according compilation title. AI News Text: {text}\n\nAnd proceeded to generate titles for the remaining 600 rows in the dataset.\n\nFine-tuning an LLM for the specific task"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 7c56c9a9-0e4d-4cd0-a251-62bf6db6abd9
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "And proceeded to generate titles for the remaining 600 rows in the dataset.\n\nFine-tuning an LLM for the specific task\n\nFinetuning a language model is a supervised form of training, requiring a labeled dataset, on which a subset of the models\u2019 parameters are retrained to learn a specific task. This form of training is far cheaper to compute, since it doesn\u2019t involve modifying the entire set of model weights, and is well suited for tuning a model to a specific kind of output."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "Typical use cases of fine-tuning include adopting a writing style, efficient text compression, producing structured output such as JSON, and adopting task specific lexicon when the original training dataset didn\u2019t have enough variety."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "State-of-the-art code for parameter efficient fine-tuning algorithms can be found in HuggingFace\u2019s excellent PEFT library. While the basic flow of implementing your own code to do fine-tuning with PEFT is pretty straightforward, you just instantiate a LoraConfig object with a few training parameters, and tell it to run off of a base model, executing this code is no small feat. You can see in PEFT\u2019s own examples that fine-tuning a model as small as 3B parameters will use more than 14GB of video RAM. This can be alleviated with CPU offloading, but then training times will suffer. And this is just for the 3B parameter model, 7B models are shown to require as much as 55GB VRAM. Access to"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "but then training times will suffer. And this is just for the 3B parameter model, 7B models are shown to require as much as 55GB VRAM. Access to server-grade compute GPUs alleviates these issues."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "While all this is doable, we\u2019ll follow a more streamlined approach, that will both save us time and spare us trouble in the future.\n\nFine-tuning with Predibase\n\nWe want to go the easy route this time, so we\u2019ll use Predibase for both the fine-tuning and the inference. Predibase is an LLM infrastructure provider for Open Source models, enabling its customers to serve customized language models at a fraction of the costs other services offer."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "Moreover, their platform streamlines the fine-tuning process using state-of-the-art approaches such as LoRA and QLoRA, making experimentation and deployment as simple as a few clicks. Their compute instances include NVIDIA A100 80GB VRAM GPUs to finetune your models on.\n\nAs of the writing of this post, inference of a smaller 7B model on their infrastructure will cost 50 times less per input token than GPT-4 Turbo, and 150 times less per output token. You can simulate your savings by making a copy of this handy calculator we made. With a system similar to ours making 100k requests/day you can save ~$1200 per day from API calls only."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "For the task at hand, we wanted to push the capabilities of the platform, so we trained 4 different model variants (we actually trained 10, but we\u2019ll share the most interesting here):\n\nllama2-13b-chat , an instruction tuned 13B parameters version of Meta\u2019s Llama2.\n\n, an instruction tuned 13B parameters version of Meta\u2019s Llama2. mistral-7b-instruct , an instruction tuned 7B param surpassing llama2-13b in several benchmarks.\n\n, an instruction tuned 7B param surpassing llama2-13b in several benchmarks. zephyr-7b-beta , an already finetuned version of mistral-7b using Direct Preference Optimization."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 02776c5a-3ebb-4504-b22f-f126935d59c0
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": ", an already finetuned version of mistral-7b using Direct Preference Optimization. mixtral-8x7b-instruct-v0-1 , this one is the odd one out since it\u2019s in a different weight class and its serving costs makes it less attractive for use. It\u2019s a 47B parameter model but the interesting thing is it\u2019s achieving similar performance to much heavier models such as Llama2-70B and GPT-3.5 so we wanted to see how it fared in the comparison.\n\nTraining these models using Predibase is a breeze. You upload your dataset (or connect one of the many available cloud storage provider integrations) to create a data connection:"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 17c405f0-38ab-43d8-99e3-3f2403c8e670
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "Once your data is connected, creating a new model task is as simple and streamlined as selecting your parameters:\n\nPredibase allows for both full custom model training (adjusting all model weights) as well as fine-tuning. Start by creating a model repository to organize your workflows. Then select your dataset, the base model you want to fine tune and introduce the prompt template. The prompt template uses a simple wildcard logic where you can replace any content with a dataset column by enclosing it in curly brackets. For example {text} gets replaced with the value of the text column for each row in the selected dataset."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 24b73362-0478-4e47-bb75-08d906b5c6c1
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "Finally, you choose the target column the model should aim to replicate, and select whether you want to use QLoRA with 4 or 8 bit quantization, or no quantization LoRA. We tried all and found very little performance difference between them, hence we suggest going with the cheaper and faster QLoRA 4bit quantization, at least for quick experimentation.\n\nAnd that\u2019s it! Your model is trained and ready to serve with this code snippet:\n\nInquisitive readers might have realized that we get the model for \u201cTWIAI Titles\u201d .\n\nTWIAI stands for This Week in AI, our weekly curated list of significant AI news."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 7b2dd5a3-bd10-4bad-8ff2-21edaff02f37
encoding: FLOAT32
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "TWIAI stands for This Week in AI, our weekly curated list of significant AI news.\n\nThat\u2019s right, we use tools such as these to automatically catalog and summarize the content, but don\u2019t worry, human curators revise, prioritize and decide which news are worthy of attention.\n\nEvaluating model performance: beyond manual inspection\n\nWith cost-effective inference models at our disposal for information retrieval tasks, a critical question arises: How can we ascertain the quality of the generated titles?\n\nThe challenge of evaluating LLMs"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 775d08cf-47d3-4e64-a034-d1b0389ad20a
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "The challenge of evaluating LLMs\n\nEvaluation of LLMs remains a complex area of ongoing research. The challenge lies in quantitatively capturing behaviors such as \"understanding the meaning\" or \"adhering to the prompt,\" which are inherently qualitative. Although manual inspection is often considered the most reliable method for assessing LLM outputs, striving for a degree of objectivity in our evaluations is essential.\n\nPredibase\u2019s built-in metrics"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 2972c0f7-fe0a-44e2-9a8a-37512dbea04a
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "Predibase\u2019s built-in metrics\n\nPredibase enhances this process with a suite of built-in metrics for model evaluation. These metrics, include a combined loss that accounts for various factors like ROUGE , BLEU , perplexity , and others, offering a foundational layer for objective analysis and model training.\n\nPredibase Learning Curves\n\nCustom evaluation with BERT-Score"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 22f4d9ea-23d6-4aba-8633-53c7290cca43
encoding: FLOAT32
metadata:
{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "Predibase Learning Curves\n\nCustom evaluation with BERT-Score\n\nYet, to align our evaluation closer with our specific needs\u2014ensuring the generated titles accurately reflect the articles' main points\u2014we turned to bert-score, a contextual similarity metric. BERT-score assesses how closely the meanings of two texts align by calculating microsoft/deberta-xlarge-mnli embeddings (a model derived from BERT) for each text's tokens. By employing cosine similarity, it measures the closeness of tokens in encoding space, with proximity indicating similarity in meaning."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "For each input token, cosine similarities are calculated, and the most closely related output token is identified. Finally similarities are averaged across all input tokens to produce the final metric. The authors propose 3 pivotal metrics: precision (the relevance of the generated title to the original text), recall (how comprehensively the title covers the original content), and the F1 score (a harmonic mean of precision and recall). A perfect score of 1 in these metrics signifies an ideal match. For our problem we\u2019ll be focusing on precision as our primary metric of interest. This choice stems from our goal to determine how well the generated title retains the original text's core"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "as our primary metric of interest. This choice stems from our goal to determine how well the generated title retains the original text's core information."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: dae32ed2-2589-4311-a264-c76600e82a70
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "Analyzing the results\n\nNow, let's delve into the data. We begin by comparing the performance metrics of GPT-4 against 400 manually curated labels:\n\nThe plot shows on the x-axis the score (Precision/Recall/F1) and on the y-axis how many of the produced titles achieved that score, red bars represent the non-curated GPT-4 titles and green bars represent the manually curated titles. The more scores we have closer to the right, the better."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 5167569a-9e9b-4bad-a214-6f1b609c709a
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "Don\u2019t take these face-value, but in principle, all curated score distributions are shifted to the right, indicating that both our own subjective assessment of better titles produced by the curation and the objective metric, agree that the curation captures more of the original meanings than the zero shot titles that GPT-4 came up with.\n\nMoving on to the finetuned models, we\u2019ll focus on the precision metric only, and compare GPT-4 (in red) to all finetuned models:\n\nTest Precision: zero-shot GPT-4 titles vs finetuned small language models."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 8a1bb4c6-7208-45b1-ad59-8f48502c84a4
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "Test Precision: zero-shot GPT-4 titles vs finetuned small language models.\n\nYou can see that, save mistral-7b which produced a few empty titles resulting in 0 Precision, all finetuned precision distributions are slightly shifted to the right. mixtral-8x7b-instruct-v0-1 is the best performing model in terms of this average precision but the much smaller and chepear zepyhr-7b-beta is so close behind that we\u2019re opting for it. You can find average and standard deviation of precision for each of these in the Test Precision Stats sheet.\n\nSubjective evaluation and conclusion"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 24cf873b-faa1-444c-989c-9adc9db8a94e
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "Subjective evaluation and conclusion\n\nWe said that manual evaluation was best for these models. So let\u2019s take a look at some of the examples produced by zephyr and GPT-4. This is a sneak peek of the data, you can find the full data in the Test Manual Comparison sheet of this doc:\n\nSubjective evaluation.\n\nThis conclusive evidence aligns our objective metrics with subjective evaluations, consistently demonstrating that the smaller model frequently generate titles of comparable or superior quality.\n\nConclusion"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "Conclusion\n\nThus concludes our comprehensive exploration: from sourcing a publicly available dataset and initially employing a high-cost, generalist LLM for rapid, zero-shot auto-labeling, creating an initial synthetic dataset, to refining quality through manual curation, and ultimately fine-tuning a smaller LLM for efficient and precise inference in production settings."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 72f9cde3-3e4b-48dc-bcbe-01746972b438
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "Further work could involve refining the prompt into shorter a less specific version, for additional savings now that our model has been finetuned. Another avenue could be enriching the synthetic data generation process by building a retrieval augmented generation system that would feed articles and titles similar to the one being summarized instead of the generic examples we provided in the prompt.\n\nBalancing AI performance and cost"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 048ca7a6-5e78-4f13-aa44-234fbc7835f0
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "Balancing AI performance and cost\n\nNavigating the complexities of AI integration into your business operations doesn't have to be a daunting task. With the right approach, leveraging advanced LLMs for data labeling and subsequently fine-tuning less expensive models allows for the exploitation of GenAI's potential in a manner that is both impactful and cost-efficient. At Tryolabs, our mission is to transform this potential into your competitive advantage."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 242e6d84-c7b5-403e-afbe-7aef07cbe763
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{"title": "Fine-tuning LLMs for cost effective GenAI inference at scale", "link": "https://tryolabs.com/blog/finetuning-llms-for-cost-effective-genai-inference-at-scale", "language": "en", "description": "Explore the advantages of fine-tuning LLMs for scalable, cost-effective GenAI inference in our comprehensive guide. Learn how to leverage GPT-4 for initial high-quality data labeling, and subsequently using Predibase to fine-tune more economical models for specific tasks.", "chroma:document": "We are committed to crafting bespoke AI solutions that not only meet but exceed your unique business requirements. Our expertise lies in identifying and executing the most effective strategies for each project, ensuring that cost-effectiveness is not just an option but a priority. Let us guide you through the process of making AI work for you \u2014 efficiently and economically.\n\nReady to take the next step? Let\u2019s talk!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: f011224a-4d61-4729-ae8e-10353ed21583
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{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "In the ever-evolving landscape of AI, the demand for powerful Large Language Models (LLMs) has surged. This has led to an unrelenting thirst for GPUs and a shortage that causes headaches for many organizations. But what if there was a way to use LLMs efficiently without relying solely on scarce GPUs?\n\nNumenta, a Bay Area-based company with nearly two decades in neuroscience research \u2014 possibly recognized for the influential book A Thousand Brains: A New Theory of Intelligence published by its Co-Founder \u2014 has recently launched its first commercial product: NuPIC."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 7aced81d-bc28-441d-a0df-5c0db19a97e8
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{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "The name stands for \u201cNumenta Platform for Intelligent Computing,\u201d and it's an LLM acceleration platform that applies neuroscience principles to achieve remarkable speedups on CPU-based LLM inference, promising even to surpass GPU performance in many scenarios.\n\nSource: Numenta."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 75cf00fb-cbf5-4921-b634-b711a8511dbd
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{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "Source: Numenta.\n\nThrough a partnership with Numenta, we got early access to NuPIC and in this blog post, we will explore the platform\u2019s architecture and then delve into its capabilities, showing you how to solve a classical use case of semantic search. To test the CPU vs GPU claims, we will do comparative benchmarks against Hugging Face's BERT on CPU and GPU and set the stage for discussing its pros, cons, and potential applications.\n\nJoin us on this exploration as we examine NuPIC, the claims surrounding its performance, and its potential role in LLM inference."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: cca6023d-d157-40a8-bf9d-ba2707701d37
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "Join us on this exploration as we examine NuPIC, the claims surrounding its performance, and its potential role in LLM inference.\n\nFull disclosure: Tryolabs has partnered with Numenta to support the development of NuPIC. Our team worked closely with Numenta engineers to design and implement key infrastructure for NuPIC's modules, including the Inference Server and Training Module. This hands-on involvement has provided us with valuable technical expertise and experience using NuPIC. While we aim to provide an objective view of the platform, our collaboration with Numenta is relevant to disclose upfront.\n\nBut what is NuPIC?"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 078fe817-2709-4ffc-9ae7-9537d3ad9742
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{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "But what is NuPIC?\n\nNuPIC aims to accelerate LLM inference on CPUs. But what does that look like in practice? It currently consists of two docker containers, which allow easy integration into any AI inference infrastructure:\n\n1. NuPIC Optimized Inference Server\n\nThis server runs on a CPU and presents a simple interface to get LLM model inference for different NLP tasks, such as classification, Q&A, or embeddings. Key features:\n\nKey features:\n\nHandles multiple simultaneous requests , as each inference step runs in a single core, and several simultaneous models without additional configuration."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 2d464728-fe0a-4959-a370-c11c9fca038b
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": ", as each inference step runs in a single core, and several simultaneous models without additional configuration. Achieves better throughput than an NVIDIA A100 GPU when running on a 4th gen Intel Xeon CPU (according to this case study) by leveraging the AVX-512 and AMX instruction sets.\n\nNuPIC is delivered with a set of optimized BERT and BERT Large models.\n\n2. NuPIC Training Module\n\nThis module runs on GPUs and allows fine-tuning the optimized models on your own data.\n\nKey features:\n\nImproves application accuracy.\n\nOutput models can be transferred to the Inference Server to start using in your application."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: cc48e1a1-34d7-40cd-afa7-ccbeec915e86
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{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "Key features:\n\nImproves application accuracy.\n\nOutput models can be transferred to the Inference Server to start using in your application.\n\nThe NuPIC Training Module includes a Weight & Biases integration for experiment tracking.\n\nWith NuPIC's architecture in mind, let's see it in action for a semantic search use case.\n\nUsing NuPIC's SBERT for semantic search\n\nImagine you're a developer building a content-rich website for a travel agency. Your goal is to empower users to find their dream destinations effortlessly, making the most out of the intent in their words.\n\nSetting the scene: the power of semantic search"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 4a720a89-9c83-44d4-a956-9ca27b400c9c
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{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "Setting the scene: the power of semantic search\n\nIn the world of travel, words carry a world of meaning. You want users to type in their travel desires, whether it's \"beach paradises,\" \"cultural escapades,\" or \"adventurous getaways\u201d, and receive tailored recommendations that capture the essence of their desires. Enter semantic search \u2013 the art of understanding not just keywords but the intent behind them. But the question is: how to perform this intent understanding?\n\nThe challenge: machine intent-understanding"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: b2989382-4f1d-40c1-98e6-5212cce44f1c
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "The challenge: machine intent-understanding\n\nTraditional keyword-based search engines often fall short when capturing semantic nuances; that\u2019s why an AI approach is more suitable here, given the recent rise of LLMs and their interpretation power."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: c978852b-18c8-4588-8c0e-9bce5076f3b9
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "A BERT-based model is appropriate for this task, as they are specifically trained to represent text with embeddings \u2014 a mathematical representation that encapsulates the contextual meaning of the text. In particular, Sentence-BERT, or SBERT for short, is a powerful language model specifically trained to better understand the structure of whole sentences, much more than its predecessors. Thus making it doubly appropriate for retrieving meaningful embeddings and paving the way for a more intelligent and intuitive search experience.\n\nThe solution: NuPIC's SBERT"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: c6c550fb-2a47-4ab2-a0fe-2b864f714d37
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "The solution: NuPIC's SBERT\n\nPre-trained magic : NuPIC's SBERT model was pre-trained on a vast text corpus, including travel guides, blogs, and reviews. This means it understands the intricacies of travel lingo, from the excitement of discovering hidden gems to the tranquility of sunset beach strolls."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 9ea1de6c-ef6e-49e1-b36f-8e176b564c92
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": ": NuPIC's SBERT model was on a vast text corpus, including travel guides, blogs, and reviews. This means it understands the intricacies of travel lingo, from the excitement of discovering hidden gems to the tranquility of sunset beach strolls. Embeddings unleashed : Let's say a user searches for \"secluded mountain retreats.\u201d NuPIC's SBERT converts this query into an embedding that captures the essence of this phrase."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: e1132ced-6330-4374-b280-8e62fa084421
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": ": Let's say a user searches for \"secluded mountain retreats.\u201d NuPIC's SBERT converts this query into an that captures the essence of this phrase. Mapping meanings : Your website's content, from travel package descriptions to user reviews, is also transformed into embeddings using SBERT and stored into a vector dataset. Now, it's not just about matching keywords; it's about matching meanings."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: ad2aa449-0725-43e3-8723-488b4d6a6b4d
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": ": Your website's content, from travel package descriptions to user reviews, is also transformed into embeddings using SBERT and stored into a vector dataset. Now, it's not just about matching keywords; Tailored recommendations: You can provide excellent recommendations by retrieving the closest embeddings to the user request! But you're not just providing recommendations based on keyword matches; you're offering suggestions that resonate with the user's intent. If users seek the serenity of mountains, they won't be bombarded with options for bustling city breaks.\n\nImplementation steps"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 7868538e-973c-45ca-9b8d-f5f980d9f7cc
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "Implementation steps\n\nNow, we'll guide you through implementing your search functionality using NuPIC's SBERT model. From infrastructure setup to real-time embeddings and personalized recommendations, let's explore the journey to enriching user interactions and understanding their intent.\n\n0. Getting access to NuPIC\n\nAs of September 2023, NuPIC is available, and you can contact Numenta to request a demo.\n\n1. Setting up the inference server: deploy NuPIC\n\nTo get started, you'll need a Linux machine equipped with an Intel CPU that supports the AMX instruction set, at least 6GB of RAM, and has Docker installed."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "Then, you must download NuPIC\u2019s Inference Server container using the Numenta-provided download.sh script and run it with the run_inference_server.sh script.\n\nThis server will play a vital role in generating embeddings and facilitating semantic searches.\n\n2. Building your knowledge base: vector dataset population\n\nVector Datasets, such as Chroma or Pinecone, are a great solution to store, manage, and query embeddings. You must configure your vector dataset and keep all the travel guide summaries as embeddings."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 0ea40547-6d13-479e-adcb-1246f00c79ad
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{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "You'll need to convert the travel guide summaries into embeddings using NuPIC's SBERT model to achieve this. Thankfully, it\u2019s very straightforward: here is an example code of how to generate the embeddings using the NuPIC client in Python for a fixed set of sentences:\n\nThe generated embeddings capture the contextual meaning of the text and lay the foundation for your semantic search functionality.\n\n3. On-the-fly semantics: real-time embedding generation\n\nEnable your backend system to instantly convert user input into embeddings. For this purpose, take advantage of NuPIC's Python client, which simplifies interaction with the NuPIC Inference Server."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "By making a single API call using this client, your system can swiftly generate embeddings in real-time in the same way as in the previous step.\n\n4. Matching meanings, not just keywords: content retrieval"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: e65e1be0-8b86-4920-bee6-31948483f65f
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{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "With the user input transformed into an embedding, proceed to query the vector database with it to get the content closest to the input embedding. This content aligns with the user's intent and allows you to pull the corresponding travel guide information. Now, present these tailored recommendations in an appealing and user-friendly manner on your website. By following these steps, you can harness the capabilities of LLMs to craft a semantic search experience that's both intuitive and intelligent. This approach transcends traditional keyword-based search engines, allowing you to offer your users personalized interactions that truly grasp the essence of their request."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: c0f8f001-636a-4da5-9c4e-93db02433fcc
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{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "This semantic search example is just one simple use case of NuPIC. You can improve this same example by merging duplicate requests or prioritizing them by importance. Or you can move and enhance other applications, such as Question & Answering (Q&A), and speed up the retrieval of important information in contracts or legal documents. You can also use techniques like Retrieval-Augmented Generation (RAG) for the BERT models, to add specific domain knowledge and reduce hallucination in an LLM.\n\nBenchmarking NuPIC against BERT"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 95b40a49-11fe-454b-bb65-e986d1400b67
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metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "Benchmarking NuPIC against BERT\n\nNow, let's put NuPIC to the test by comparing its performance against Hugging Face's BERT on CPU and GPU. We'll conduct a straightforward experiment to test the efficiency and speed of NuPIC.\n\nYou can find all the code and instructions in this repo.\n\nExperiment setup\n\nWe'll use the Financial Sentiment Analysis dataset and measure the time required to generate embeddings for each model:\n\nNuPIC's SBERT Large Hugging Face's BERT Large (CPU) Hugging Face's BERT Large (GPU)\n\nAll CPU experiments ran on a 4th Generation Intel Xeon from a m7i.2xlarge AWS EC2 instance, while the GPU experiment ran on a NVIDIA A100 .\n\nNuPIC's SBERT"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 6f14d7a8-d17a-425c-9008-15e86b0a3039
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "NuPIC's SBERT\n\nFirst, we need to import and setup the NuPIC's Client to connect to our running NuPIC Inference Server. Then, we iterate over the sentences and use NuPIC's SBERT to get the embedding. Finally, we measure the time it takes with the following script:\n\nHugging Face's BERT on CPU\n\nTo make a fair comparison to NuPIC, we will use Hugging Face\u2019s BERT Large model, which has almost the same architecture as the numenta-sbert-2-v2 model we used before. We will only use one thread for inference as the NuPIC Inference Server assigns one thread for each request."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: dc0a35a8-eef9-4ab8-aafc-c74a64e67c40
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "First, let's import the necessary libraries. Next, load the BERT model and tokenizer required. Finally, measure the time it takes to run the inference from text to tokens and to embedding on the whole dataset.\n\nHugging Face's BERT on GPU\n\nLastly, let's measure the inference time for Hugging Face's BERT on GPU, by simply replacing the \"cpu\" with \"cuda\" at the beginning of the previous script. Remember that this experiment also uses a batch size of 1 for inference.\n\nResults and comparison\n\nNow that we've measured the inference time for NuPIC's SBERT, Hugging Face's BERT both on CPU and GPU, we can compare the results, which show the efficiency of NuPIC's SBERT for CPU-based NLP tasks."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 26aa33e3-9f96-4e9c-9958-5356bb88f70d
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "NuPIC HF (CPU) HF (GPU) Execution time 21.2ms 302.9ms 23.6ms Max hourly throughput * ~1.358.000 ~95.000 ~1.220.000 Cost per 1M requests * * 1.18 16.84 26.80\n\nThe CPU experiments were run on an AWS EC2 m7i.2xlarge instance, and the GPU experiment was run on Google Colab with an NVIDIA A100 . * With a maximum of 8 concurrent requests or a batch size of 8. * * Using AWS as a reference, with a p4d.24xlarge as GPU instance."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 6d6f0ed3-07e1-44e1-b286-4a0104134571
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "In our experiments, NuPIC's SBERT demonstrates a remarkable 15x speed for CPU inference time and a 10% speed improvement when compared with a GPU, and between 14 and 22 times cost reduction, making it a compelling choice for CPU-based LLMs, especially in scenarios where GPU resources may be limited or costly to deploy."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 771ed1d1-e518-4ec0-bfff-1b6c15bf810e
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "An important point to remark is that NuPIC handles each request independently and does not require any additional batching to make an efficient use of the computational resources as the GPU would. Another important point is that NuPIC can handle several models simultaneously while keeping the same throughput, while GPUs have significant throughput drops whenever you need to use a different model. This means that in practice, the throughput gains of NuPIC can be much higher than what this benchmark shows."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 9a822556-39cd-4b94-80be-6b0812c09152
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "How to Scale NuPIC We tested this to ensure NuPIC's scalability using infrastructure as a code: We began by creating a template instance with the NuPIC inference server, ensuring that every new instance inherits the same configuration and settings.\n\nThen, we set up the autoscaling service to allow us to adjust the computational resources to match real-time demand.\n\nAnd finally, we set the required network services as the load balancer, firewall, and proxies. The result? A scalable LLM solution that effortlessly handled surges in workload without breaking a sweat."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: ecfca16f-474e-4dec-a680-dd7137cdc80f
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "So, there you have it, folks! NuPIC stands out as it achieves impressive 15x speedups and allows for an easy integration, allowing to deliver practical and efficient LLM solutions running on a CPU. Now let's point out some Pros and Cons of NuPIC.\n\nPros and Cons of NuPIC\n\nNuPIC offers notable benefits but also has some limitations to consider, as summarized below:"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 91ede166-9563-4985-b3ff-e1b983ffd3d3
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "Pros and Cons of NuPIC\n\nNuPIC offers notable benefits but also has some limitations to consider, as summarized below:\n\nPros Cons Privacy and control: runs in your infrastructure Current version has variants of BERT out of the box Much faster CPU inference speeds Newer platform with less integration support Better price/performance ratio than GPUs Documentation still being worked on Avoids GPU shortage and scaling issues Manual process for existing models Fine-tuning module\n\nPros"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 7da5b1ad-febc-4082-95a1-dc6ad09505c5
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "Pros\n\nPrivacy and control . Runs fully in your infrastructure so you maintain control over data and models. Whether you operate in the cloud or prefer an on-premise environment, NuPIC's privacy policy allows you to make strategic decisions aligned with your organization's unique needs."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 082430cc-9c19-4397-89f1-b5bf4fdb1714
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": ". Runs fully in your infrastructure so you maintain control over data and models. Whether you operate in the cloud or prefer an on-premise environment, NuPIC's privacy policy allows you to make strategic decisions aligned with your organization's unique needs. CPU efficiency . Optimized to achieve much faster LLM inference on CPUs vs GPUs. In our benchmarking experiments, NuPIC achieved much faster LLM inference speeds on CPUs compared to BERT on both CPU and GPU."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: d147e612-26f4-4443-a055-7ce56dbde20e
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": ". Optimized to achieve much faster LLM inference on CPUs vs GPUs. In our benchmarking experiments, NuPIC achieved LLM inference speeds on CPUs compared to BERT on both CPU and GPU. Cost-effectiveness . Better price performance ratio compared to expensive GPUs. A cost-effective solution could be achieved without compromising performance by using CPUs for LLM.\n\n. Better price performance ratio compared to expensive GPUs. A cost-effective solution could be achieved without compromising performance by using CPUs for LLM. Availability . Avoids GPU shortage issues. Can scale CPU resources more easily."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: eb310df9-21bf-4544-a8f9-8522d98a1614
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": ". Avoids GPU shortage issues. Can scale CPU resources more easily. Fine-tuning module. NuPIC includes a simple training module that allows you to fine-tune NuPIC models with your own data while keeping all the performance optimization.\n\nCons\n\nIt is a Paid Framework . As we\u2019ve said before, you\u2019ll need to contact Numenta\u2019s team to get your copy of NuPIC. You\u2019ll need to do the math based on the pricing and the money you\u2019ll be saving on GPUs (which are expensive)."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 7e7a345a-f6a0-42cf-9e10-79f1bb6a40ea
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": ". As we\u2019ve said before, you\u2019ll need to contact Numenta\u2019s team to get your copy of NuPIC. You\u2019ll need to do the math based on the pricing and the money you\u2019ll be saving on GPUs (which are expensive). Current version has variants of BERT out of the box, with GPT models likely coming in the future. If you are currently working with a different model architecture, you should consider moving to a BERT-like model to get the full optimization that NuPIC offers."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 405162e6-73f8-4b94-a203-3f401310bce0
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "with GPT models likely coming in the future. If you are currently working with a different model architecture, you should consider moving to a BERT-like model to get the full optimization that NuPIC offers. Emerging community . As a newer platform, NuPIC has an emerging user community. While resources are still limited today, early adopters can help shape future growth through real-world usage and feedback."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 0e65bad2-ec01-43ce-98a8-ce50a728f78d
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": ". As a newer platform, NuPIC has an emerging user community. While resources are still limited today, early adopters can help shape future growth through real-world usage and feedback. Manual process for existing models. You can't just plug-and-play existing models yet. You will need the help of Numenta to put your model through a manual process to convert it to NuPIC format.\n\nClosing thoughts"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 372379dd-d461-444c-8efd-6ca7b6efef8a
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "Closing thoughts\n\nIn this exploration and testing of NuPIC, we've managed to examine the results of neuroscience being applied to AI, achieving notable LLM inference speeds on CPUs. With experiments and usage examples, we've benchmarked NuPIC's capabilities to enhance inference speed, specifically for semantic search.\n\nNuPIC does make a leap in reducing hardware limitations for language models. Be it conversational AI, personalized recommendations, or other language-based AI applications. This is a new tool, so what could the future bring along? Exciting times to be in the field of AI inference!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: e405cb5c-d7d9-45ba-a3d5-aa189776abda
encoding: FLOAT32
metadata:
{"title": "From Brain to Binary: can neuro-inspired research make CPUs the future of AI Inference?", "link": "https://tryolabs.com/blog/from-brain-to-binary-cpus-future-ai-inference", "language": "en", "description": "NuPIC is transforming NLP inference on CPUs. Learn how this innovative platform achieves remarkable speedups rivaling GPU performance. We dive into real-world impact across use cases like semantic search and text classification.", "chroma:document": "At Tryolabs, we have hands-on experience with NuPIC and are eager to guide you on this new frontier of efficient LLM inference. Our team stays on the cutting edge of AI advances so we can deliver strategic guidance and custom solutions leveraging the latest innovations like NuPIC. Contact us to learn how we can help you build transformative AI applications enhanced by next-generation technology."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: d8a5eafc-caa2-4fe4-be28-bf4a082c365a
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "The challenge of setting the right price\n\nSetting the right price for a good or service is an old problem in economic theory. There are a vast amount of pricing strategies that depend on the objective sought. One company may seek to maximize profitability on each unit sold or on the overall market share, while another company needs to access a new market or to protect an existing one. Moreover, different scenarios can coexist in the same company for different goods or customer segments."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 5f3cb3d6-f76e-4680-b9c7-dbf4682a217f
encoding: FLOAT32
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "In this blog post, we\u2019ll present the problem of price optimization for retail \u2013 which has its own particularities \u2013 and how retailers can take advantage of the tremendous power of Machine Learning technology to build effective pricing solutions.\n\nThese are some of the crucial questions that retailers recurrently face: What price should we set if we want to make the sale in less than a week?\n\nWhat is the fair price of this product, given the current state of the market, the period of the year, the competition, or the fact that it is a rare product?"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: dbe39245-f0e4-41af-9530-e08c9e8acde9
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Given that these days it is very easy for a customer to compare prices thanks to online catalogs, specialized search tools or collaborative platforms, retailers must pay close attention to several parameters when setting prices. Factors such as competition, market positioning, production costs, and distribution costs, play a key role for retailers in order to make the right move. Check this example for a deep dive into a real-life sales data analysis for an online retailer."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Machine Learning can be of great help in this case and have an enormous impact on KPIs. Its power lies in the fact that the developed algorithms can learn patterns from data, instead of being explicitly programmed. Machine Learning models can continuously integrate new information and detect emerging trends or new demands.\n\nThe use of Machine Learning is a very attractive approach for retailers. Instead of using, for example, aggressive general markdowns (which is often a bad strategy), they can benefit from predictive models that allow them to determine the best price for each product or service.\n\nLet's talk about building new solutions Let's talk\n\nWhat is price optimization?"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 2bfd898f-f622-4613-9627-c7c0f1231435
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Let's talk about building new solutions Let's talk\n\nWhat is price optimization?\n\nBriefly, price optimization uses data analysis techniques to pursue two main objectives:\n\nUnderstanding how customers will react to different pricing strategies for products and services, i.e., understanding the elasticity of the demand. Finding the best prices for a given company, considering its goals.\n\nPricing systems have evolved since the early 1970s until now, from applying very simple strategies, such as a standard markup to base cost, to being capable of predicting the demand of products or services and finding the best price to achieve the set KPI."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Understanding consumer preferences can have several uses besides optimizing prices. If this picks your interest, take a look at this article where we modeled customer demand to perform assortment optimization in a large B2B Consumer Packaged Goods company, delivering a 10 million dollar profit increase in the first six months, representing a 2% uplift of total profits.\n\nPrice optimization techniques can help retailers evaluate the potential impact of sales promotions or estimate the right price for each product if they want to sell it in a certain period of time.\n\nCurrent state-of-the-art techniques in price optimization allow retailers to consider factors such as:\n\nCompetition\n\nWeather"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Current state-of-the-art techniques in price optimization allow retailers to consider factors such as:\n\nCompetition\n\nWeather\n\nSeason\n\nSpecial events / holidays\n\nMacroeconomic variables\n\nOperating costs\n\nWarehouse information\n\nto determine:\n\nThe initial price\n\nThe best price\n\nThe discount price\n\nThe promotional price\n\nUsing different kind and sources of data to find the prices that improve profits.\n\nPrice optimization vs dynamic pricing\n\nEven though sometimes these two concepts are used as synonyms, they represent different concepts. The main difference is that dynamic pricing is a particular pricing strategy, while price optimization can use any kind of pricing strategy to reach its goals."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "For example, using a dynamic pricing strategy, retailers can dynamically alter the prices of their products in order to match their competitor's price. This strategy would imply changing prices very frequently but not necessarily being this the best strategy possible. Price optimization techniques focus on finding the price that maximizes a defined cost function (e.g., the company's margin), considering many different factors to suggest such price or price range for different scenarios. Depending on the particular use case, this can indeed be performed in a dynamic way, and thus combining dynamic pricing + optimization is the go-to option for many scenarios."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: a598ed65-e8cf-4ec5-b7bf-2db382726c86
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "In some highly competitive scenarios where anticipating demand is hard, incorporating dynamic pricing hand crafted rules into a pricing system can allow to take quick advantage of certain market situations. Stay tuned for the next blogpost in our pricing series in which we\u2019ll show a case study where we combined Dynamic Pricing with Continuous Experimentation to increase profits in a large tech powered Amazon e-tailer.\n\nPrice optimization vs automatic pricing\n\nMoreover, it is important to differentiate price optimization from automatic pricing as they primarily solve two different pain points: sub-optimal pricing strategy vs. excessive cost of pricing."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "The main difference is that we focus on a price automation solution when pricing is a pain point for the company in terms of costs. By automatically pricing the items we are not changing the pricing strategy itself but we are changing the pricing process making it cheaper and faster. On the other hand, when we think of a price optimization solution we change the pricing strategy in order to maximize an objective function, subject to different business constraints."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "This is to say that by implementing a price optimization solution we are automating our pricing process but not vice versa; not necessarily all price automation solutions optimize the pricing strategy. Both price automation and price optimization solutions could be understood as dynamic pricing if the frequency of price changes is high."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 80d33ee4-f85d-4896-8d89-82b55ae14673
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Autopricing on its own can have a huge impact on a company\u2019s bottom line, due to the capacity of Machine Learning models to simultaneously consider vast amounts of information from disparate sources. If you want to take a look at how we leveraged Machine Learning to incorporate non-traditional sources of information into a Real Estate pricing solution, check this article in our pricing series.\n\nPrice automation with and without Machine Learning"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 516a5135-a9c9-481c-bfdd-ae1ebf4e35c4
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Price automation with and without Machine Learning\n\nFinally, price automation can be developed with or without Machine Learning. The difference between these two approaches is that without Machine Learning the pricing rules are pre-defined while with Machine Learning rules are obtained in a data-driven way.\n\nFor example, a price automation system without using Machine Learning would take the form of a pre-defined set of rules such as:\n\nMark up all products in FOOD CATEGORY by 15%\n\nIf BRAND is in MARKETING CAMPAIGN add a 10% discount\n\nIf KEY VALUE ITEM set price equal to competition and lower it by 5%\n\nAdjust all prices to end with \u201c.99\u201d"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 245fcbe4-e880-4728-af60-4d919791d5fd
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "If KEY VALUE ITEM set price equal to competition and lower it by 5%\n\nAdjust all prices to end with \u201c.99\u201d\n\nOn the other hand, a price automation solution with Machine Learning implies training a model capable of automatically price items the way they would be priced by a human expert at scale. The model could take in historical data and different characteristics of the product as well as unstructured data such as images and text and would learn the pricing rules with no explicit coding, adapting to changes in the environment in a much richer and dynamic way.\n\nKey differences between different pricing concepts"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 10b6e857-b6a9-4873-94e3-69be67b4fdab
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Key differences between different pricing concepts\n\nFeature Dynamic Pricing Price Optimization Price Automation with ML Prices change frequently Yes Yes Yes Prices are set automatically Yes Yes Yes Main goal is to reduce pricing process cost - - Yes Main goal is to optimize pricing strategy - Yes - Demand Forecasting - Yes - Estimate Store/SKU price elasticity of demand - Yes - \"What if\" Pricing Scenario Capability - Yes -\n\nWhat Machine Learning can do for retail price optimization"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 66a642bb-b8f2-4003-976b-8e1c75bddff3
encoding: FLOAT32
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "What Machine Learning can do for retail price optimization\n\nThe pricing strategies used in the retail world have some peculiarities. For example, retailers can determine the prices of their items by accepting the price suggested by the manufacturer (commonly known as MSRP). This is particularly true in the case of mainstream products. Another simple strategy is keystone, which consists in defining the sale price as double the wholesale price or cost of the product."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 84ca93e4-3528-44a8-a2b3-660d653d5d5d
encoding: FLOAT32
metadata:
{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "While these and other strategies are widely used, Machine Learning enables retailers to develop more complex strategies that work far better to achieve their KPIs. Machine Learning techniques can be used in many ways to optimize prices. Let's have a look at a typical scenario.\n\nFor example, a widely adopted pricing strategy technique that enhances this technology is dynamic pricing. Using this strategy, retailers can dynamically alter the prices of their products based on current market demand. However, changing the prices dynamically with no objective function in mind may lead to suboptimal results. This is why we suggest using dynamic pricing jointly with price optimization techniques."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 4aa28d13-1a09-4203-ae0b-5d5f7497722f
encoding: FLOAT32
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "A typical scenario\n\nImagine an e-commerce or brick-and-mortar retailer who wants to estimate the best prices for new products for the next season. The competition is hard, so their prices and promotions need to be taken into consideration. Therefore, the retailer adopts a widely used strategy: competitive pricing. Simply put, this strategy defines the price of a product or service based on the prices of the competition.\n\nLet\u2019s see the steps needed to develop a Machine Learning solution for this use case.\n\nProcess of defining prices in retail with price optimization using Machine Learning.\n\n1. Gather input data"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 44d65cb5-acd9-4060-80ad-f9bddf4f6ec2
encoding: FLOAT32
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Process of defining prices in retail with price optimization using Machine Learning.\n\n1. Gather input data\n\nFirst of all, we need data. To train Machine Learning models, it is necessary to have different types of information (structure or unstructured data):\n\nTransactional : a sales history that includes the list of the products purchased and, eventually, the customers who purchased them.\n\n: a sales history that includes the list of the products purchased and, eventually, the customers who purchased them. Description of the products : a catalog with relevant information about each product such as category, size, brand, style, color, photos and manufacturing or purchase cost."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 18b85f69-6015-4b36-b47c-f265a7cd15d1
encoding: FLOAT32
metadata:
{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": ": a catalog with relevant information about each product such as category, size, brand, style, color, photos and manufacturing or purchase cost. Data on past promotions and past marketing campaigns.\n\nand past marketing campaigns. Customer Reviews : reviews and feedback given by customers about the products.\n\n: reviews and feedback given by customers about the products. Data on the competition : prices applied to identical or similar products.\n\n: prices applied to identical or similar products. Inventory and supply data .\n\n. In the case of physical stores: information about their geographical location and that of the competitors."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 9c937d2c-92d7-4eee-a47d-83670466f3ef
encoding: FLOAT32
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": ". In the case of physical stores: information about their geographical location and that of the competitors.\n\nFor further reference take a look at this post that highlights actionable steps to get your data ready for price optimization.\n\nDepending on the set KPIs and the way of modeling the solution, some of this data may not be necessary. For example, if there is little or no information about customers, which is sometimes the case for brick-and-mortar retailers, the model can nonetheless be trained."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: de611683-c9ff-443c-8308-77572be153cb
encoding: FLOAT32
metadata:
{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "In contrast, information about the competition is crucial for a competitive pricing strategy. In many cases, it is even possible to connect via APIs to this information or monitor it online. Checkout a real-life data collection example here.\n\n2. Define goals and constraints\n\nThe next step is to define the strategic goals and constraints.\n\nRetailers may pursue a unique, clear objective of profit maximization. However, they may also be interested in customer loyalty (e.g. increasing the net promoter score or the conversion rate) or in attracting a new segment (e.g. young people)."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: fc0572f3-7368-49f1-9daf-c32f2aeea59d
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Restrictions may be of legal nature (e.g. if some type of control of sale prices is carried out), they may have to do with the reputation of the company (e.g. fearing a bad image for applying favorable prices only to a certain segment of customers) or be related to physical aspects such as the capacity of a store or the average time of supply.\n\nEach particular scenario will impact the way the problem is modeled. It is possible, and usually very interesting, to test different scenarios for the same retailer, which implies using different models.\n\n3. Modeling and training"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: f9dc33f0-ef9b-42f1-a6a8-70f0d4ca36dd
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "3. Modeling and training\n\nIn this step, the data previously gathered is used to train the Machine Learning models. There is a wide variety of models that can be used in price optimization. Historically, Generalized Linear Models (GLMs) have been used (in particular, logistic regression). However, for a few years, more complex and powerful methods have been developed. For instance, depending on the volume of data available, it could be possible to use Deep Learning methods or even reinforcement learning techniques."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 9751ae53-4a5c-4b03-ae88-2c94a6e2e061
encoding: FLOAT32
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "In this case, in which we are dealing with new products for the next season, there is an additional difficulty since there is no previous product data. The interesting thing is that the Machine Learning models will know how to find similar products and be effective despite not having specific prior data. The same happens in the case of retailers that sell rare or exotic products.\n\nYou can take a look at a real-life example of demand forecasting modeling here.\n\n4. Execute and adjust prices"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 8850aafa-52bf-4b3f-8fb1-59eef011ac60
encoding: FLOAT32
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "You can take a look at a real-life example of demand forecasting modeling here.\n\n4. Execute and adjust prices\n\nOnce the model is trained, prices can be estimated for the new products and tested. Depending on the modeling, the estimate may be an exact price or a range. The prices obtained by the model can be subsequently adjusted manually by the retailer and optimized regularly.\n\nMore opportunities of using Machine Learning for price optimization"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: a8657164-aa25-44db-a7ea-04aefd2094d6
encoding: FLOAT32
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "More opportunities of using Machine Learning for price optimization\n\nMachine Learning can be used for other tasks related to pricing in retail. For example, given a new product, a clustering algorithm can quickly associate it with similar products to obtain a probable price segment. Another compelling possibility is to jointly predict prices and demands for items that were never sold.\n\nMore generally, Machine Learning can be a tremendous tool for insights:\n\nIn what way is the sale of pants impacted when shirts\u2019 prices are drastically cut?\n\nWhen efforts are made to sell more pens, are the related products, such as ink, notebooks or work agendas, impacted?"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 9b572d6b-ff7c-42e4-8f02-c4c442425f9d
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "When efforts are made to sell more pens, are the related products, such as ink, notebooks or work agendas, impacted?\n\nAre customers who buy a certain computer more or less likely to buy monitors the following month?\n\nAre clients inactive in the last year sensitive to a promotion campaign?\n\nThese are just some examples of the questions that Machine Learning models can help answer.\n\nAdvantages of price optimization with Machine Learning\n\nIn addition to automation and speed, there are several advantages to using Machine Learning to optimize prices."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 83f4a73c-eebb-4e03-bada-33398ca5601e
encoding: FLOAT32
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "In addition to automation and speed, there are several advantages to using Machine Learning to optimize prices.\n\nFirst, Machine Learning models can consider a huge number of products and optimize prices globally. The number and nature of parameters and their multiple sources and channels allow them to make decisions using fine criteria. This is a daunting task if retailers try to do it manually, or even using basic software."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 493cb66f-e651-4080-9291-f2b6c5b4fa67
encoding: FLOAT32
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "For example, it is known that changing the price of a product often impacts the sales of other products in ways that are very hard to predict for a human. In most cases, the accuracy of a Machine Learning solution will be significantly higher than that of a human. In addition, retailers can modify the KPI and immediately see how the models recalculate prices for the new goals.\n\nSecond, by analyzing a large amount of past and current data, a Machine Learning can anticipate trends early enough. This is a key issue that allows retailers to make appropriate decisions to adjust prices."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "This is of particular importance both for new products for which we have no past price reference and for existing products in dynamic markets whose price characteristics change over time. Well designed pricing algorithms can exploit this by solving a problem known as the Exploration vs Exploitation tradeoff, where the algorithm aims to balance exploiting the currently known good prices, with exploring other promising pricing regions that may arise. If you want to know more, you can take a look at this article where we apply a Reinforcement Learning agent capable of balancing exploration and exploitation to pricing. You can even play a game to try to beat our pricing algorithm!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Alternatively, you can also follow our Mastering Pricing Strategies series: in its first volume, we explain in depth the optimization problem, and then define a simulated market on which we can evaluate these algorithms on fair grounds. Then the second volume is a deeper dive, where we show how we resolved the Exploration vs Exploitation problem for a more advanced class of algorithms that can leverage more complex data."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Finally, in the case of a competitive pricing strategy, Machine Learning solutions can benefit from systems that can continuously crawl the web and social media to gather valuable information about prices of competitors for the same or similar products, what customers say about products and competitors, and a competitor's deals for certain products, as well as their price history over the last number of days or weeks.\n\nA system that can learn most of what is happening in the market allows retailers to have more information than their competitors in order to make better decisions.\n\nPrice optimization for brick-and-mortar and e-commerce retailers"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: a6f6ebd1-a5ec-422e-8f7c-66d31bbc75b9
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Price optimization for brick-and-mortar and e-commerce retailers\n\nWhile it may seem more natural to apply Machine Learning in the case of e-commerce retailers, brick-and-mortar retailers can perfectly take advantage from this technology.\n\nIn fact, price changes are less often performed in brick-and-mortar retailers and thus, having more room to improve and adjust to current demand.\n\nDigital price tags are enabling brick-and-mortar retailers to do as many price changes as e-commerce sites. However, even without digital price tags, weekly or monthly price changes can be performed in order to match the current demand and maximize profit."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "We have proven our approach with one of the largest travel retailers in the world with over 400 stores across the globe and over 160 million clients per year. We helped them boost gross margin by 28% performing weekly price changes in-store.\n\nPrice optimization and demand forecasting with Machine Learning during a crisis"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 71064f67-76c0-44c5-8c49-1ab9422592fd
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Price optimization and demand forecasting with Machine Learning during a crisis\n\nIn May 2023, the World Health Organization officially declared the end of the COVID-19 global public health emergency. However, back in 2020, we observed its direct impact on consumer spending. Businesses all over the world had to revise their annual sales forecasts and strategies. They were not operating in a business as usual (BAU) manner, leading to the question: are we still able to use Machine Learning to predict demand in completely unexpected scenarios like these?"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Our answer is \u201cyes, but with new things to consider\u201d. In a BAU scenario, Machine Learning models are likely to leverage historical sales and correlated external data to bring insights such as seasonality, relevant sales dates, and competitors' reactions. During a crisis, as the market is not behaving as usual, the historical insights are likely to fall short to predict future sales. To fight back, we\u2019d need to increase the importance of shorter-term information (e.g., daily sales), in the understanding that the recent past is much more suitable to predict the future. Practically, this means adjusting the feature engineering process to weigh the shorter-term sales lags rather than the"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "to predict the future. Practically, this means adjusting the feature engineering process to weigh the shorter-term sales lags rather than the historical ones."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Additionally, the demand forecasting problem will also require the incorporation of more real-time market data than before as well as external macroeconomic and social data. On the real-time data, this means regularly updating available market data such as sales data, customer churn, sales intent (e.g. added to cart items, traffic to competitors\u2019 sites), competitors' prices, among others. On the macroeconomic level, data such as consumer spending, unemployment, GDP and even community mobility segmented by cities/regions could also be considered, although these are mostly reported on a monthly basis. Stock market indicators (S&P 500, Dow Jones) could potentially be considered too, as a proxy"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 7c910272-ceb9-4cfd-97b1-faa61d6e051a
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "although these are mostly reported on a monthly basis. Stock market indicators (S&P 500, Dow Jones) could potentially be considered too, as a proxy of real-time macroeconomic trends. Finally, there might also be positive results by incorporating social data, if we take the pandemic as a use case, we could use the reported COVID cases or current government policies (i.e. lockdown duration), to generate scenario forecasting and consider them for modeling future demand."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 6598756f-6070-4dc3-8e21-b4f152a786bc
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "To summarize, in any abruptly new economic scenario, our analysis is still that Machine Learning could be greatly leveraged to build accurate demand forecasts and optimize the pricing strategy. The key adaptations to a BAU scenario would be to incorporate more real-time data (market and macroeconomic data) + adapt the models to consider nearer-term lags vs. historical data. It\u2019s also worth noting that business understanding and human judgment will still play a key role in the creation of this solution.\n\nCompanies using Machine Learning for price optimization"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Companies using Machine Learning for price optimization\n\nPrice optimization has been used, with significant success, in industries such as hospitality, airline, car rental, and e-commerce retail.\n\nOne of the first success stories occurred in the early 2000s, when Hilton Hotels Corp and InterContinental Hotels Group decided to eliminate fixed rates in favor of a fluid scheme, including dynamic pricing strategies. In those years the prices of the rooms were modified once or twice a day. The current computational power allows prices to change practically in real time."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: bb580cae-936e-4e5c-89d9-410c7b3fe81c
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "The hotel industry continues to employ dynamic pricing strategies, based entirely on Machine Learning. Currently, Airbnb proposes a dynamic price tool that recommends prices to its hosts, considering parameters such as seasonality, the day of the week or special events, and also more sophisticated factors such as photos of the property to be rented or the prices applied in the neighborhood. Other companies such as eBay and Uber have adopted similar approaches."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: ddc622cf-e8a3-4039-8237-950905bdf511
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Amazon is another of the big players when talking about dynamic pricing strategies. To give an idea, in 2012 Amazon was changing prices much more often than its competitors and in 2013, they were performing as much as 2.5 million price changes per day. Changing prices in such a dynamic way is informally known as the Amazon effect.\n\nIn the retail world, the most popular examples have been in e-commerce, but brick-and-mortar retailers have not been left behind. Although it is difficult to know precisely all the retail companies using Machine Learning to optimize their prices and operating processes, there are nevertheless some known success stories."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 494f66d7-bd6d-4c4b-b83b-d35bfc16459b
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Companies like Ralph Lauren and Michael Kors use Machine Learning to offer fewer markdowns and better manage their inventory, seeking to increase profit margins, even at the risk of losing a little revenue. Another well-known case is that of Zara, which uses Machine Learning to minimize promotions and adapt quickly to the changing trends. There are many other success stories, such as Morrisons \u2013one of the largest supermarket chains in the United Kingdom\u2013, bonprix \u2013an international fashion company based in Germany\u2013 or Monoprice \u2013an American B2B and B2C electronics retailer\u2013, among others. While there is no information available on the exact modeling of the problems, it is known that these"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: d6fe79c1-a2d2-4693-82b8-290f2f12eb92
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "and B2C electronics retailer\u2013, among others. While there is no information available on the exact modeling of the problems, it is known that these companies are taking advantage of the power of Machine Learning to increase their revenues and improve operations."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: d8af07b1-ec42-4109-8f7a-464c8448b328
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Get the latest news about price optimization Subscribe to our pricing with ML newsletter.\n\nFinal thoughts\n\nNowadays the world is moving towards changing prices more often and using state-of-the-art data driven pricing strategies is a must. In a study performed by Bain & Company they show that top performers across industries are nearly twice as likely to price dynamically.\n\nWhether it's about an e-commerce marketplace or a brick-and-mortar retail store, both are embracing the benefits of dynamic pricing and price optimization."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 11d24acd-b925-40cc-ad56-57fb0e69e296
encoding: FLOAT32
metadata:
{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Price optimization helps retailers understand how customers will react to different price strategies for products and services, and set the best prices. Machine Learning models can take key pricing variables into account (e.g. purchase histories, season, inventory, competitors\u2019 pricing) to find the best prices, even for vast catalogs of products or services, that can achieve the set KPIs.\n\nThese models don\u2019t have to be programmed. They learn patterns from data and are capable of adapting themselves to new data. They allow retailers to quickly test different hypotheses and make the best decision."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: a49ad54b-7897-43ca-bd98-60e90291c5c3
encoding: FLOAT32
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "What is probably most important to keep in mind is that the use of Machine Learning in the retail world keeps widening, and all signs point to the fact that this trend will continue in the coming years.\n\nThe question is no longer whether to apply dynamic pricing or not. But the question is how to do so in order to remain profitable.\n\nDive deeper into pricing strategies with Tryolabs\n\nHere at Tryolabs, we specialize in Machine Learning solutions for retail companies. Since 2010, we have been working with several retailers, which let us better understand the opportunities, challenges and available solutions within the industry."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 5269a61d-fb18-4a53-baf7-f34685eceab6
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{"title": "How Machine Learning is reshaping Price Optimization", "link": "https://tryolabs.com/blog/price-optimization-machine-learning", "language": "en", "description": "An introduction to Price Optimization, its opportunities with Machine Learning, and how retailers can use automated pricing solutions to increase revenue.", "chroma:document": "Want to stay ahead in the competitive retail landscape? Explore our Pricing Collection for a curated set of content tailored to inform and inspire.\n\nFor a more hands-on, tailored experience, consider our in-company AI Talk on Price Optimization. Our experts visit your teams, delivering insights and strategies on how AI-driven dynamic pricing can elevate your business.\n\nReady to harness the power of data-driven pricing? Contact us, and together we'll chart your path to pricing excellence."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 0977a6c1-35d8-4f1d-be70-88dc8fca43d2
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Demand forecasting and price optimization can help businesses make informed decisions regarding production quotas, inventory, and pricing. It can be a daunting task to obtain real-world data to inform those decisions. We discussed the ever-growing role of Machine Learning in solving these kinds of problems in a previous blog post. And we also provided practical examples of one of our working pipelines. Now, in this post, we will focus on a tool that can assist you in developing these strategies: our Market Simulator.\n\nWhy build a market simulator?"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 461aefd2-c474-4ff1-8d28-1951144138d1
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Why build a market simulator?\n\nLet's start with a quick overview. Demand forecasting is the process of predicting the future demand for a product or service. It involves analyzing historical data, market trends, and consumer behavior to estimate the demand for a particular product or service. Price optimization, for its part, is the process of determining the optimal price for a product or service. It involves considering factors such as demand forecasts, competitors' prices, and production costs to determine the optimal price point to maximize profits. These are the issues most often encountered by our partners when they reach out to us for help."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: ceba32da-b7de-4df3-9f23-74daa0a58d31
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Throughout this journey, we've dealt with many different problems. And while each case was unique, they all presented some common denominators we managed to isolate and incorporate into our own set of internal pricing tools. One of these tools - our pricing suite - takes all the lessons learned along the way into account and can be easily repurposed from project to project, allowing our teams to provide effective and tailored solutions much faster. We'll be sharing more about this tool in our upcoming blog, so keep your eyes on this space! Experience has also proven that staying competitive in competitive markets requires significant research, so we started to develop our Market Simulator."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "We designed this tool to obtain accurate and risk-free scenarios to test our models and apply our solutions. We aim to loosely represent many known market situations of interest and evaluate how each model performs in the given business case. Our simulator can receive the market conditions to model along with the model we want to test. And this enables us to comprehensively analyze the given model's performance within the selected context."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: a39dd233-d2e0-47b7-86c3-accc78be43c9
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Our goal when we started, was to develop a tool to help us further understand how our pricing models learn and adapt to an ever-changing reality. The tool's primary purpose is to evaluate if we were appropriately leveraging our pricing model's strengths and managing their weaknesses. It's invaluable for experimenting with new ideas to enhance our solutions further. It has proven essential in our efforts to continuously improve our strategies and provide the best pricing models available. In the next section, we'll get into the nitty-gritty of our design process, so it might get a bit more technical, but hang in there; we promise it'll be worth it!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 06ec5a8c-af51-4b7a-bd34-6328fca7c238
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "To sum it all up, with a market simulator, we can: Emulate different market conditions\n\nTest any desired model\n\nQuickly evaluate and tweak all new approaches in a risk-free environment\n\nThe ins and outs of building the simulator"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 39579bce-e438-41d0-8531-f6672c795d8c
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "We built a controlled simulation of a single-item market, with unlimited supply, to accomplish the above. We specify the actual underlying demand function and configure our models to be able to find it based on their observation of market outcomes. The selected model is presented with limited historical data and must make a pricing decision based only on that data. We then set the chosen price in the market and observed the simulated customers\u2019 response to that price (i.e., the number of units they purchased). That, in turn, becomes a new data point that can help us further understand demand, so we repeat the cycle, training our model repeatedly over time."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Figure 1: Diagram of the simulation cycle."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Our market simulation is built from a single random variable (RV): the customer\u2019s demand ( Q ) (Q) (Q). This RV takes positive integer values, so we modeled it following a Poisson distribution (we\u2019ll provide more details about this later). What characterizes this distribution is its expected value, which depends on the features that conform to the market state. These features always include price, and can include any other feature that may be of interest like seasonal effects, seller ratings, shipping times, etc. For simplicity of this exposition, we\u2019re going to model the expected number of units sold in the market ( Q ^ \\hat{Q} Q^\u200b) depending on the price ( p p p), using the following"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "we\u2019re going to model the expected number of units sold in the market ( Q ^ \\hat{Q} Q^\u200b) depending on the price ( p p p), using the following equation:"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Q ^ ( p ) = \u03b1 1 + e ( \u03b2 \u00d7 p + \u03b3 ) ( 1 ) \\hat{Q}(p) = \\frac{\\alpha} {1 + e^{(\\beta \\times p + \\gamma)}} \\qquad \\qquad (1) Q ^ \u200b ( p ) = 1 + e ( \u03b2 \u00d7 p + \u03b3 ) \u03b1 \u200b ( 1 )\n\nWhere:\n\n\u03b1 \\alpha \u03b1 , also known as the market size parameter, controls the overall height of the curve,\n\n, also known as the market size parameter, controls the overall height of the curve, \u03b2 \\beta \u03b2 controls the slope of the elastic region,\n\ncontrols the slope of the elastic region, \u03b3 \\gamma \u03b3 controls the location of the inflection point."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "controls the slope of the elastic region, \u03b3 \\gamma \u03b3 controls the location of the inflection point.\n\nFigure 2: a) Here we can see how \u03b1 affects the overall height of the curve. b) Here we can see how \u03b2 affects the slope of the elastic region. c) Here we can see how \u03b3 affects the location of the inflection point.\n\nIf we wanted to model factors other than price affecting sales, instead of just a constant, \u03b1 \\alpha \u03b1 becomes \u03b1 ( X ) \\alpha(X) \u03b1(X), any function of the covariates X X X.\n\nIn particular, we can effectively model \u03b1 ( X ) \\alpha(X) \u03b1(X) as a simple linear combination of the covariates, with the coefficients we choose representing the importance of each covariate:"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "\u03b1 ( X i ) = \u03b1 0 + \u2211 i = 1 N F \u03b1 i \u00d7 X i ( 2 ) \\alpha(X_i) ={\\alpha_0+\\sum_{i=1}^{N_F}\\alpha_i \\times X_i} \\qquad \\qquad (2) \u03b1 ( X i \u200b ) = \u03b1 0 \u200b + i = 1 \u2211 N F \u200b \u200b \u03b1 i \u200b \u00d7 X i \u200b ( 2 )\n\nWhere:\n\nX i X_i X i \u200b is a vector of size N F N_F N F \u200b of additional features affecting demand\n\nThe power of simplicity: While simple, this formulation is rather flexible, allowing for the representation of multiple market realities as shown below."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: a54e862e-b0b6-4c24-bfd6-4efd8d5c0f90
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "For example, to represent perishable products, values of \u03b1 = 1 , \u03b2 = 1 , \u03b3 = \u2212 8 \\alpha=1, \\beta=1, \\gamma=-8 \u03b1 = 1 , \u03b2 = 1 , \u03b3 = \u2212 8 allow formulating a demand curve with a terminal velocity, where prices below 2 saturate demand and you don\u2019t get any more sales even while continuing to decrease prices (Figure 3.a)."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 20eee767-accf-4781-af05-83313ee8cc94
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "allow formulating a demand curve with a terminal velocity, where prices below 2 saturate demand and you don\u2019t get any more sales even while continuing to decrease prices (Figure 3.a). On the contrary, values of \u03b1 = 2 , \u03b2 = 0.5 , \u03b3 = \u2212 0.5 \\alpha=2, \\beta=0.5, \\gamma=-0.5 \u03b1 = 2 , \u03b2 = 0.5 , \u03b3 = \u2212 0.5 produce a curve that keeps increasing sales units as price decreases (Figure 3.b).\n\nFigure 3: a) Example of the demand curve with \u03b1=1, \u03b2=1, \u03b3=-8. b) Example of the demand curve with \u03b1=2, \u03b2=0.5, \u03b3=-0.5.\n\nSimulating demand"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 52a75ce1-a245-448f-8203-686eee74b4a1
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Figure 3: a) Example of the demand curve with \u03b1=1, \u03b2=1, \u03b3=-8. b) Example of the demand curve with \u03b1=2, \u03b2=0.5, \u03b3=-0.5.\n\nSimulating demand\n\nWe obtain the simulated expected demand as a function of price, the blue curve shown below, by plotting the above-stated demand function. And it shows that a slight shift in price near the inflection point can cause a substantial change in demand. The further away we get from the inflection point, the less sensitive demand is to price fluctuations. This formulation corresponds then to a demand curve with terminal velocity.\n\nFigure 4: The expectation of the simulated demand function. We should notice it is modeled as a logit."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: de8220fc-5bf9-436b-a74b-d6a603291205
encoding: FLOAT32
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Figure 4: The expectation of the simulated demand function. We should notice it is modeled as a logit.\n\nCalculating the expected profit function is straightforward, as we now have an established expected demand function. Taking a specific cost of production or acquisition, c, into account, we can define our profit as a function of the expected demand as follows:\n\nP r o f i t ( q ) = ( p \u2212 c ) \u00d7 q ( 3 ) Profit(q) = (p - c) \\times q \\qquad \\qquad (3) P ro f i t ( q ) = ( p \u2212 c ) \u00d7 q ( 3 )"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 2db7d9a3-6d03-411a-b8fd-bdac74b20b4f
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "P r o f i t ( q ) = ( p \u2212 c ) \u00d7 q ( 3 ) Profit(q) = (p - c) \\times q \\qquad \\qquad (3) P ro f i t ( q ) = ( p \u2212 c ) \u00d7 q ( 3 )\n\nNotice that profit is also a random variable because it depends on the demand. And since profit is a linear function of demand, the linearity of expectation allows us to calculate its expected value easily.\n\nP r o f i t \u203e ( q ) = ( p \u2212 c ) \u00d7 q \u203e ( 4 ) \\overline{Profit}(q) = (p - c) \\times \\overline{q} \\qquad \\qquad (4) P ro f i t \u200b ( q ) = ( p \u2212 c ) \u00d7 q \u200b ( 4 )\n\nNow that we've defined the expected profit in terms of the expected demand, we can add its plot to the previous one and determine the optimal price - our (expected!) profit."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: d47a430d-54cd-419b-991d-a39e7874c393
encoding: FLOAT32
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Notice that the observed profit may be higher than the expected profit for a particular realization. In the long run, the variability should be canceled out, and the expected gain should be met.\n\nFigure 5: Here we can see the simulated expected demand function as a constant blue curve and its profit function as a constant red curve. We can see the profit function's y-axis to the right of the plot. We are also highlighting the profit function's maximum with a blue dot. That blue dot is at the maximum profit achievable with our expected demand function. The price value corresponding to this point is thus the optimal price in this scenario."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 9ea3c6b9-48b0-47ae-9b3d-43954ecde40d
encoding: FLOAT32
metadata:
{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "This approximates a given market situation for a particular product with an established price range and some X i X_i Xi\u200b features. In other words, we defined the expected sales rate for a given market situation X X X, and price p p p , (the target variable of all the demand curve regression problems). The data we have produced so far represents the ground truth we want to get as close to as possible with our chosen model\u2019s predictions. But to generate any predictions, we are going to need data."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: e8574ff1-fb68-4e10-bbfb-bd181b517908
encoding: FLOAT32
metadata:
{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Every demand forecasting project is built with data. So we now need to create historical data we can work with later. We could simply randomly sample Q ^ \\hat{Q} Q^\u200b. But, in the real world, we can't observe the expected sales rate of a product directly. What we see instead is a noisy random realization of said rate. To emulate this, we should model our historical data as a sampling from a Poisson distribution using Q ^ \\hat{Q} Q^\u200b as the average rate at each price. In Figure 6, we can see the resulting historical data points in green. In this case, we sampled prices to cover the whole price range, but in practice, this likely isn\u2019t going to be the case."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: e5a8fc4d-67f5-4268-bae0-659f2abff938
encoding: FLOAT32
metadata:
{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Figure 6: Poisson sampled data from the known simulated expected demand function.\n\nUnderstanding demand curves\n\nLet's quickly recap: We now have a known ground truth expected demand function, a known expected profit function with its corresponding optimal value, and some noisy observational data to work with. What's next?"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 3b60964e-82d1-4293-aa4e-ce48dd3e5179
encoding: FLOAT32
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "The next step is to estimate the demand curve from our noisy observations using an appropriate demand forecasting model. To try and solve this regression problem, we'll give it a go with our old friend, XGBoost. So we train our regression model of choice on the available historical data (the green dots) and use that data to predict an estimated demand curve ( Q ^ \u2217 \\hat{Q}^* Q^\u200b\u2217). The regression model will target the number of units sold, as a function of price and any other covariates of interest we have access to. An example of the resulting predicted demand is displayed in light blue below. Once we have obtained an estimated demand function, we can calculate the corresponding forecasted"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: f79d664b-cd4d-4a95-b5f5-70e31d537300
encoding: FLOAT32
metadata:
{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "predicted demand is displayed in light blue below. Once we have obtained an estimated demand function, we can calculate the corresponding forecasted profit as mentioned in Eq 4."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: aeb2ba13-a0fa-4b14-a53a-dc16070e3f34
encoding: FLOAT32
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "In the following plot, we can see the aforementioned estimated profit function as a pink curve with an orange dot at its maximum value. The maximum estimated profit value observed corresponds to the predicted optimal price. With a more complex optimization problem, we could also consider things other than profit, such as how long a product took to sell and the stock available. But to keep things simple, we'll just focus on profit optimization and recommend that the price be set at the aforementioned predicted optimal price."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: dbe0c2e7-e394-4bdc-a83b-5405374fafc6
encoding: FLOAT32
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Notice that the suggested price given by the model is somewhat higher than the actual optimal price. That's because our information is imperfect: While close to the actual underlying expected demand, our estimate has a margin of error, as all Machine Learning models do. In this case, it's underestimating the demand near the optimal price points, making the profit smaller than expected. We can also see regions where it overestimates the demand, making the estimated profit bigger than it should be.\n\nFigure 7: Forecasted demand (light blue curve) tries to estimate the expected demand (blue curve), while forecasted profit (pink curve) tries to estimate the expected profit (red curve)."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Tying things together"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 23f3045b-f993-46b3-b6b4-fe2d1ed83718
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "With all we've covered so far, we can create sales for any given period and in different scenarios. Our goal was to simulate market dynamics in a daily re-pricing scenario. All that's left is choosing the suggested price and observing how it fares. To that end, we created an algorithm where on t = 0 t=0 t=0, we use our historical data to predict the optimal price relative to the historical data available. Remember that the optimal price, in this case, is the price that will maximize the expected profit, which is deduced from the model's demand curve estimate. Once we obtain the optimal price, we set it as the sale price for the item and simulate the observed demand by sampling from a"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: dcefb2a5-718b-4d98-bae4-41cf70e89734
encoding: FLOAT32
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "demand curve estimate. Once we obtain the optimal price, we set it as the sale price for the item and simulate the observed demand by sampling from a Poisson distribution with the known expected value given by Eq 1 at the recommended price point. By chaining all this together, we end up with the following procedure:"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 856fd954-3dc7-430a-a966-16e2a86c6878
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "For every t t t in the simulation horizon:\n\nTrain the demand model with all the historical data up to t t t and obtain Q ^ \u2217 \\hat{Q}^* Q ^ \u200b \u2217 , the demand forecast Simulate market features X t X_t X t \u200b for the current time step t t t Optimize forecasted expected profit using Q ^ \u2217 \\hat{Q}^* Q ^ \u200b \u2217 , X i X_i X i \u200b and all possible price points to get p \u2217 p^* p \u2217 Set p \u2217 p^* p \u2217 in the market and simulate a number of sales by sampling from Q ^ \\hat{Q} Q ^ \u200b the realized sales units q q q Add the ( p \u2217 , X i , q ) (p^*,\\: X_i,\\: q) ( p \u2217 , X i \u200b , q ) point as a new data point Increase time step and repeat"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 545c7f7c-bf53-4b65-8206-0a6f9cb4d2f7
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "If we were to repeat this process for a fixed amount of time, we would get the results shown in the animation below (Figure 8). Neat, right? With animations like this, we can easily track our model's progress through time in any scenario. We can also compare how different strategies fared in the same conditions (when fed the same data)."}


created_at: 2024-05-14 14:30:36
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "As we can see from our example, we start with a misguided pricing situation. In simulation step 0, all of our historical prices are way below the optimal price, and that's reflected in the resulting profit we obtained. But, if we didn't have our ground truth to compare with, we wouldn't even know this. We could only glean the market responses to the prices we set. However, as we let our model do its job, given enough time, it will suggest a new optimal price, so we end up with a good approximation of our known Q ^ \\hat{Q} Q^\u200b in the region of interest."}


created_at: 2024-05-14 14:30:36
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "If you\u2019re wondering why the suggested price doesn\u2019t go into the price > 15 region, that\u2019s because we bound price exploration to a region not too far away from historic prices. This is to avoid the model extrapolating too much into a region where no data is available.\n\nFigure 8: Re-pricing simulation. Here we show how our model learns the underlying demand through the historical data and suggests a new optimum price that we then use in the simulated market."}


created_at: 2024-05-14 14:30:36
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Looking back at the first steps of our simulation, you can see that our model suggests increasingly higher prices at the beginning of the process. As we try those prices, our model gets new input. And this new input demonstrates that our profit grows as we increase our product\u2019s price. But only up to a certain point, after which the decrease in demand counters the price increase. Once that limit is reached, our profit starts to decrease again. It takes the model a few tries, but it quickly learns that the optimal price resides somewhere in the middle. Moreover, it does so by producing higher profits in each step than all the ones tried before.\n\nLooking at our simulation metrics"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Looking at our simulation metrics\n\nOur 15-minute experiment enabled us to collect some interesting metrics."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Our 15-minute experiment enabled us to collect some interesting metrics.\n\nFor starters, we can measure the profit lost at each step (Figure 9.a). This calculation shows how the profit we generated by choosing the predicted optimal price compares to the profit we could have made had we known the accurate optimal price every time. That value is meant to display the cost of having imperfect information. If we knew the actual underlying demand curve, we would always be able to achieve the real optimal profit. Because we simply don't have this curve, the best we can do is estimate it and use our estimate to forecast profit. This metric is also known as the model\u2019s regret."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "The difference between the true profit curve and the estimated one leads to profit losses, which we want to minimize. We start with a significant profit difference, and that's accurate because we began with prices that were way off the mark. We then see a reduction in the losses a few iterations later as our model tries out new prices, learns more, and moves closer to the optimal price. It's clear that while the model started with prices that were way off, it nonetheless managed to find the interval of the true optimal price and price consistently around it, reducing the profit losses at each step and converging to almost 0 profit lost per step."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Another plot we included is the percentage of cumulative profit losses (Figure 9.b). Here we illustrate the ratio of the total incremental profit loss up until the current simulation step. What percentage did we lose out of the total optimum profit we could have made thus far? That gives us an idea of the total profit lost throughout the simulation due to imperfect information. Even with the optimum price oscillations we end up with, we still have a very low profit loss percentage."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "We were interested in tracking two additional values as the simulation progressed: the difference between the actual and predicted optimal values of both the price and the demand (Figures 9.c and 9.d). The results obtained for the run shown in the animation above are shown in the following plots. We can see that the absolute difference gets smaller in both cases as time goes by.\n\nFigure 9: a) Profit lost in each iteration step. b) Percentage of cumulative profit lost. c) Price \u0394 between the optimum price and the predicted optimum price. d) Demand \u0394 between the optimum demand values and the predicted optimum demand values."}


created_at: 2024-05-14 14:30:36
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "We also calculated the mean error (Figure 10.a) and the root mean squared error (Figure 10.b) for the predicted demand ( Q ^ \u2217 \\hat{Q}^* Q^\u200b\u2217) and the expected demand ( Q ^ ) (\\hat{Q}) (Q^\u200b) using a separate set of datapoints, independent from the actually tried prices, that aims to cover the entire input space of the demand function. Ideally, we would have wanted a gridded sample across the entire demand input space, however, due to the curse of dimensionality the size of that grid grows exponentially with the dimension of the space, so we chose a random sample instead."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "These values can tell us how close we are to the underlying demand, considering the entire price range shown in the animation. We're off to a good start, but there is still work to be done to determine the expected demand. In this case, incomplete observations is but one of many reasons leading to errors in the estimate. The chosen model's extrapolation outside the historically tried prices - both price extremes - is a continuation of the nearest known value. Within lower-priced regions, this is manageable, but as prices grow, the predictions become consistently poor. Hence, this region skews the demand ME and RMSE values throughout the simulation, as shown in the following plots."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Figure 10: a) Demand mean error. b) Demand root mean squared error."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "A small note. If we perform our simulation, removing randomness (the Poisson samples) and instead sampling values from the expected demand directly, we get the results shown in the following animation (Figure 11). This is a much more trivial problem, since the model can learn much faster from data without noise, but in practice, this isn\u2019t realizable. Even then, we can still see that at very high prices, we have a constant prediction that matches the sales obtained for the highest price, since trees extrapolate with a constant value. Although the model quickly hits the optimal price in this scenario, our demand RMSE and ME would still be skewed because of the difference in the price range"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "the model quickly hits the optimal price in this scenario, our demand RMSE and ME would still be skewed because of the difference in the price range mentioned above (10- \u221e \\infin \u221e)."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "Figure 11: Simulation without randomness. If we take observations from the underlying expected demand (without randomness), estimating it is trivial.\n\nLessons learned, lessons shared\n\nGlad you made it this far! We hope you come away with some valuable concepts. But just to be sure, let\u2019s recap the key takeaways from our journey:\n\nWe\u2019ve built a simulator that\u2019s capable of modeling different market realities from the ground up with a simple yet very expressive formulation.\n\nWe\u2019ve formalized the problem at hand, determined how to generate the synthetic information we need, and formulated the functions we need to estimate."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "We\u2019ve implemented a Machine Learning algorithm with a data-based feedback loop to estimate both demand and profit. Said algorithm is able to converge to the desired results for the problem at hand.\n\nWe\u2019ve collected several performance metrics along the way, having the simulator become a \u201cgym\u201d for our algorithms, allowing us to compare the performance of different modeling alternatives in a controlled environment."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 8fcba6b6-c091-45c0-a512-fa5dbd952cba
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "So we built a simulator capable of modeling different market states from the ground up. And with it, we trained several variations of XGBoost models for estimating demand, and by tracking several relevant metrics that we detailed above, we can now assess how performant each of the variants is, and which one performs the best across multiple scenarios.\n\nThis simulator has enabled faster and more effective incorporation of additional tools into our pricing strategy. We can quickly determine if a new strategy will yield better results in different simulations in a risk-free environment. Only once we are confident it performs well do we include it in an actual plan with much higher stakes."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Mastering pricing strategies vol 1: Intro to our market simulator", "link": "https://tryolabs.com/blog/2023/04/13/mastering-pricing-strategies-vol-1-intro-to-our-market-simulator", "language": "en", "description": "Explore our innovative Market Simulator for demand forecasting and price optimization, designed to help businesses make informed decisions, test models, and adapt to ever-changing market conditions for enhanced performance", "chroma:document": "If this post piqued your interest, stay tuned for the upcoming volume 2! In it we\u2019ll explore some limitations of the basic learning models and how we can leverage the simulator to overcome them, particularly striking a good balance between exploration and exploitation as well as improving the model\u2019s behavior in the extrapolation regions.\n\nDo you think your organization could benefit from such a simulator? Drop us a line!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Tryolabs & Google join forces for Forecasting Code Camp at Khipu 2023", "link": "https://tryolabs.com/blog/2023/03/01/tryolabs-and-google-join-forces-for-forecasting-code-camp-at-khipu", "language": "en", "description": "Join us for Khipu.ai 2023, the AI event of the year! We're excited to be a sponsor and among the notable speakers, including experts from Apple, DeepMind, and more. Plus, don't miss our partnership with Google for a groundbreaking forecasting code camp. Register now and secure your spot!", "chroma:document": "We're thrilled to announce that we're coming back as a sponsor for the upcoming Khipu conference! This extraordinary AI event will take place from March 6th to March 10th, 2023, in Uruguay, and we can't wait to be part of it once again.\n\nKhipu is the largest AI conference in Latin America, bringing together students, researchers, industry participants, and distinguished international speakers. This community of like-minded individuals are working towards a common goal: to advance the field of artificial intelligence and shape the future of technology.\n\nWe're excited to join this community once again and connect with fellow enthusiasts who share our passion for AI.\n\nNotable speakers"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Tryolabs & Google join forces for Forecasting Code Camp at Khipu 2023", "link": "https://tryolabs.com/blog/2023/03/01/tryolabs-and-google-join-forces-for-forecasting-code-camp-at-khipu", "language": "en", "description": "Join us for Khipu.ai 2023, the AI event of the year! We're excited to be a sponsor and among the notable speakers, including experts from Apple, DeepMind, and more. Plus, don't miss our partnership with Google for a groundbreaking forecasting code camp. Register now and secure your spot!", "chroma:document": "We're excited to join this community once again and connect with fellow enthusiasts who share our passion for AI.\n\nNotable speakers\n\nWe're excited to be among speakers from around the world, including:\n\nOur CTO, Alan Descoins, is also part of this list and will be sharing his insights with attendees and learning from other industry experts. We're stoked to have him on the list of speakers! If you're at the conference, come and say hi to Alan and our team! We can't wait to connect with you and learn from the many talented folks at the event.\n\nA partnership with Google for our Forecasting Code Camp!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: aa83d23e-1992-41fc-ad2f-718b90a8fbcc
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{"title": "Tryolabs & Google join forces for Forecasting Code Camp at Khipu 2023", "link": "https://tryolabs.com/blog/2023/03/01/tryolabs-and-google-join-forces-for-forecasting-code-camp-at-khipu", "language": "en", "description": "Join us for Khipu.ai 2023, the AI event of the year! We're excited to be a sponsor and among the notable speakers, including experts from Apple, DeepMind, and more. Plus, don't miss our partnership with Google for a groundbreaking forecasting code camp. Register now and secure your spot!", "chroma:document": "A partnership with Google for our Forecasting Code Camp!\n\nWe're thrilled to announce that we are partnering with Google for our code camp \"A hands-on forecasting guide: from theory to practice.\" This unique collaboration will take place on Tuesday, March 7th, and it's an event that you don't want to miss!\n\nDuring this Code Camp, we'll provide an overview of concepts, methods, and practical applications on the M5 forecasting challenge. You'll get hands-on experience with real-world challenges and learn from experts in the field. This is a rare opportunity to explore the fascinating world of forecasting and gain valuable insights that can benefit your work and career."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 6fdf6a86-33f5-4143-9a99-c1763cc21c97
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{"title": "Tryolabs & Google join forces for Forecasting Code Camp at Khipu 2023", "link": "https://tryolabs.com/blog/2023/03/01/tryolabs-and-google-join-forces-for-forecasting-code-camp-at-khipu", "language": "en", "description": "Join us for Khipu.ai 2023, the AI event of the year! We're excited to be a sponsor and among the notable speakers, including experts from Apple, DeepMind, and more. Plus, don't miss our partnership with Google for a groundbreaking forecasting code camp. Register now and secure your spot!", "chroma:document": "We're proud to partner with Google for this event and excited to combine our knowledge and resources to bring you an exceptional learning experience.\n\nJoin our Code Camp waitlist! Join our waitlist now and take the first step toward mastering the art of forecasting. We'll be in touch soon to confirm your spot. We can't wait to connect with you and explore the fascinating world of forecasting together!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: d7021ad2-d1e0-42fd-a2cd-12f1f41045c6
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Large Language Models (LLMs) are powerful tools in the field of artificial intelligence that can understand and respond to natural language. Recently, LLMs like GPT-3, OPT, or T5 have stolen the spotlight on AI. These huge deep learning models, which consist of billions of parameters, perform astonishingly well for an extensive set of natural language processing tasks, including text generation, summarization, translation, and many more. A great example of the flexibility and power of LLMs is the recently released ChatGPT, which can produce astounding human-like text responses for a wide range of prompts."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: a9ab043c-42b2-407e-bc5b-1dd525086437
encoding: FLOAT32
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "While these large models are great for general tasks, they may need to be fine-tuned to achieve optimal performance on specific downstream tasks or when dealing with new, unseen data. Fine-tuning is when a model's parameters are adjusted to fit a particular dataset better. This can help companies use the model for tasks specific to their business.\n\nAs a treat to our amazing readers, we fine-tuned an LLM to build a chatbot that can answer natural questions about the rich content published on our blog. This article will walk you through the fine-tuning process, from building our own dataset to optimizing inference time for the fine-tuned model!\n\nSay Hi!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: ffc732ed-29ff-4ff6-b320-680918dfd5ee
encoding: FLOAT32
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Say Hi!\n\nBefore diving into the details of our fine-tuning pipeline, we invite you to try out our chatbot for yourself! We\u2019ve set up a \ud83e\udd17 Hugging Face Space where you can ask the chatbot any questions you have about this blog post. Simply follow this link to access the space and start chatting! In addition, we\u2019ve uploaded the fine-tuned model and its ONNX export to \ud83e\udd17 Hugging Face so that you can explore them at will."}


created_at: 2024-05-14 14:30:36
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "The model powering the chatbot was trained on the task of Conversational Question Answering, which means it can answer questions regarding a specific context (in this case, the content of this blog) or previous questions or answers from the current conversation thread. Try chatting with the bot in a conversational style, just as you would with one of Tryolabs\u2019 tech experts!\n\nCaveats"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Caveats\n\nRemember that this is just a small demo, so the answers may not be as accurate as expected; in the Improvements section, we\u2019ll discuss how we can enhance the model\u2019s responses! Also, the content of this blog was not included in the training set, which means that the chatbot will give you answers about new, unseen data!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Response time may take between 10 to 20 seconds due to the use of \ud83e\udd17 Hugging Face\u2019s free-tier space, which only has access to two CPU cores. This response time is slow for a chatbot being used in production, but it's fast for a CPU deployment and for such large inputs (since the model needs to process the entire blog to generate an answer). We'll discuss optimizing inference time in the Optimizing with ONNX section.\n\nThe Boom of Foundation Models"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "The Boom of Foundation Models\n\nHave you ever wondered how computers can understand and respond to human language? The answer lies in a new concept called \u2018Foundation Models\u2019. Popularized under this name by the Stanford University, these are machine learning models trained on immense amounts of data (over tens of terabytes, and growing) that can be adapted to a wide range of downstream tasks in fields such as image, text, and audio, among others."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "LLMs, a type of Foundation Model, are particularly well-established in Natural Language Processing (NLP). These models are cutting-edge artificial intelligence, capable of producing human-like language with remarkable accuracy. They can be used to generate text for various tasks such as translation, summarization, paraphrasing, question answering, and even assisted coding."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "These models come in different flavors and are often open-source. They can vary in size, the tasks they were pre-trained on, efficiency, and other characteristics. For example, the smallest LLMs have only a few million parameters, while the largest ones, such as the recently released Switch Transformers, have over a trillion.\n\nSome of the most widely adopted LLMs are:\n\nBERT , developed by Google\n\n, developed by Google BLOOM , developed by BigScience Workshop\n\n, developed by BigScience Workshop GPT-J , the open-source version of GPT-3 developed by OpenAI\n\n, the open-source version of GPT-3 developed by OpenAI OPT , developed by Meta\n\n, developed by Meta T5, developed by Google"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": ", the open-source version of GPT-3 developed by OpenAI OPT , developed by Meta\n\n, developed by Meta T5, developed by Google\n\nA comprehensive list of open-source language models can be found on \ud83e\udd17 Hugging Face\u2019s list of supported models. Thanks to \ud83e\udd17 Hugging Face and the ML community for providing this huge open-source repository!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "One recent example of the impressive capabilities of Foundation Models is ChatGPT, arguably one of the most incredible advances in AI history. This model, developed by OpenAI, is a fine-tuned version of GPT-3.5 (one of the latest versions of the GPT-3 model family). ChatGPT can be used through a simple chat interface to perform various tasks, including summarization, text generation, code generation, and question-answering on virtually any topic. What sets ChatGPT apart is its ability to produce highly detailed, complete, and human-like responses, admit mistakes and adjust replies based on the user's needs."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "As the name suggests, Foundation Models can serve as the foundation for many applications, but they require fine-tuning and adjustments to get the most out of them. So keep reading to learn how to get them to their full potential!\n\nFine-tuning an LLM\n\n1. Building a Dataset\n\nThere is a wide range of public datasets that you can use to fine-tune your model, but if you desire to improve the results for your specific task, you will probably need to build your own dataset. This might be a crucial task to achieve good results, albeit time-consuming."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Our goal was to build a chatbot that could answer questions about Tryolabs\u2019 blog posts, so we created our own custom dataset, TryoCoQA, to fine-tune a Foundation Model that can interact with Tryolabs\u2019 readers, just like yourself!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: b0686fe0-a1e5-429b-a561-cded2e4e2286
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Like the CoQA dataset for conversational question answering, TryoCoQA consists of questions and answers about a specific context in a conversational manner, with some questions and answers referencing previous points in the conversation. We aimed for natural language questions with a variety of writing styles and vocabulary, and answers that can be short spans of text extracted from the context or free-form text, hoping for the model to produce more human-like responses with high-quality content.\n\nThe dataset and guidelines for building it are available in the following GitHub repository.\n\n2. Choosing a Foundation Model"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "The dataset and guidelines for building it are available in the following GitHub repository.\n\n2. Choosing a Foundation Model\n\nSelecting the right open-source LLM to fine-tune can be a tough choice. There\u2019s a handful of them, and while they may perform quite well for a broad range of general tasks, it can be challenging to predict how well they will perform on our specific task before fine-tuning. Think of it like buying a new pair of dancing shoes - they may look great and feel comfortable, but you won't know how well they'll perform until you hit the dance floor."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "In addition to wanting the model to perform well on our specific task, we also wanted to consider other factors, such as resource efficiency and the size of the dataset required for fine-tuning. We were looking for a model that could run on minimal resources and be fine-tuned with a small dataset."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Given our goal of building a QA chatbot for our blog posts and the constraints we have outlined, we zeroed in on T5 and its variants as a promising choice. T5 stands for \u201cText-To-Text Transfer Transformer.\u201d Its philosophy is that it models every NLP task as a text-to-text problem. For example, in the task of Document Classification, T5 outputs a simple text containing the name of the predicted class for the document rather than outputting logits for all possible classes. In addition to this creative approach to NLP problems, we also liked that T5\u2019s architecture is similar to the original transformer encoder-decoder architecture proposed in the \u2018Attention Is All You Need\u2019 paper."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "One variant of T5 that stood out was FlanT5. It\u2019s been fine-tuned on a wide range of downstream tasks and over 450 datasets, and it even performs well on zero-shot tasks, meaning it can obtain good results for a new task or dataset without training on it. Just what we needed! With a minimal dataset, one could fine-tune FlanT5 for a few epochs and obtain excellent results."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Of course, ML is not always that simple: our use case required processing whole blog posts, which can be very long. T5 and FlanT5 both use the popular self-attention mechanism, which has a quadratic increase in memory consumption with input size! In other words, to process a whole blog post \u2014 or worse, a batch of them \u2014 we would need an astronomical amount of memory."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Fortunately, another variant of T5, called LongT5, saved the day! LongT5, proposed by Google, is especially designed to process large inputs. It uses a different attention mechanism called TGlobal (Transient Global) Attention Mechanism, which requires far less memory and allows LongT5 to excel at numerous tasks that other transformer architectures can\u2019t handle due to memory shortage, such as scientific paper summarization or QA about Wikipedia articles."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "So LongT5 seemed like the right choice for efficient training and inference on our dataset, but it did pose a small problem. In contrast with FlanT5, LongT5\u2019s available checkpoints weren\u2019t fine-tuned on many tasks, so it didn\u2019t perform very well on zero-shot scenarios. To address this, we fine-tuned LongT5 on a few publicly available datasets before training on our own data!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 187c1849-73f7-49e0-87ba-3d0d2311e3eb
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "In summary As you can see, selecting which model to train for your particular problem can be challenging as it generally involves many factors. So always try to consider the following: How well the model fits your specific task . You can dig deeper into the datasets and functions it was pre-trained on and do some zero-shot or few-shot rounds of evaluation on your own tasks and data."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 42057975-fc81-4fcb-b36e-532e2596555b
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": ". You can dig deeper into the datasets and functions it was pre-trained on and do some zero-shot or few-shot rounds of evaluation on your own tasks and data. The size of your inputs and outputs. Some models may not scale well for large inputs and outputs due to high memory consumption or a substantial number of operations.\n\nSome models may not scale well for large inputs and outputs due to high memory consumption or a substantial number of operations. The size of your dataset. If you have a small dataset, choose a more powerful model capable of zero-shot or few-shot learning. Nevertheless, the more data you have, the better results you will achieve."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 3a757965-efaf-4740-8b4d-dc37c376127d
encoding: FLOAT32
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "If you have a small dataset, choose a more powerful model capable of zero-shot or few-shot learning. Nevertheless, the more data you have, the better results you will achieve. How much computing power is available for training and inference. This can be a significant factor in determining which models you can use, as larger models may not fit in your memory, or training may become painfully slow. You can use libraries like \ud83e\udd17 Accelerate for distributed training to make the most out of your hardware. All this can seem overwhelming, but don\u2019t worry; here at Tryolabs, we can help you choose the right model for your needs! Contact us and get started on building your own fine-tuning pipeline."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "3. Fine-tuning strategy\n\nOnce we selected the perfect Foundation Model, the next step was to prepare our data to be able to train on it.\n\nOne of the challenges we faced was that our dataset was relatively small, which could make it harder for the model to learn how to converse and answer questions about our blog posts.\n\nBut we had a plan! To give our model a head start, we first fine-tuned it on much larger and more general datasets. This way, the model was able to learn how to retrieve answers to general questions before focusing on the specific content of our blog posts.\n\nWe selected two available datasets, SQuAD2.0 and CoQA, and trained on them following two different schemes:"}


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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "We selected two available datasets, SQuAD2.0 and CoQA, and trained on them following two different schemes:\n\nFine-tuning the model sequentially on each dataset, which means fine-tuning first on the SQuAD2.0 dataset, then on CoQA, and finally, on our dataset. Combining the datasets SQuAD2.0 and CoQA into one single dataset and fine-tuning on it, and when the training is over, fine-tuning sequentially on our dataset.\n\nCombining the datasets resulted in slightly better performance, but both alternatives produced similar results."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Combining the datasets resulted in slightly better performance, but both alternatives produced similar results.\n\nIn addition to deciding how to combine the datasets, we also needed to adapt our examples to T5's text-to-text input and output format. This involved restructuring and rewriting the context and questions in our inputs.\n\nTo refresh the reader\u2019s memory, in the use-case of Conversational Question Answering, the goal of the model is to generate the answer to a question given a context and the previous questions and answers from the conversation. With this in mind, we formatted the inputs following this structure:"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Here, the input is a text string containing the context (i.e., the content of one of Tryolabs blog posts) followed by the last two question-and-answer pairs in the conversation and the current target question, with the target output being the answer to the target question. We chose to add just the last two question-and-answer pairs to limit the amount of conversation history the model needs to pay attention to while still being able to generate coherent responses. Note that this is a hyper-parameter you can adjust when fine-tuning your own model.\n\nWith our data prepared and fine-tuning strategy determined, the final step was setting up our infrastructure environment and training the model."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 66ae5a57-7df4-49c8-96d0-7199f13a725b
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "With our data prepared and fine-tuning strategy determined, the final step was setting up our infrastructure environment and training the model.\n\n4. \ud83e\udd17 Accelerate\n\nFine-tuning a big Foundation Model can be computationally expensive, especially if you want to do it quickly. However, we can still train models with limited resources. There are various techniques to accommodate large models in small environments."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "In our case, we used 2 x NVIDIA GeForce RTX 3090 GPUs, which had a total of 48GB VRAM. Although this setup seems to have excellent computing power and memory capacity, it was not enough to train the model independently. To accomplish the task, we had to use different techniques supported by \ud83e\udd17 Hugging Face\u2019s distributed training and inference library, \ud83e\udd17 Accelerate.\n\nThe techniques that worked for us were:\n\nUsing gradient accumulation, which lets you train on bigger batch sizes as the gradients get accumulated over several batches, and the optimization step is calculated after a certain number of them."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Using gradient checkpointing to reduce memory consumption by forgetting the activations during the forward pass and recomputing them on the backward pass of each training round.\n\nPicking the right batch size to balance training speed and memory consumption.\n\nChoosing optimizers that consume less memory, such as Adafactor (the optimizer used in the original T5 and LongT5 papers).\n\nTweaking parameters in the data loader, such as pinning the memory to the CPU and setting the right number of workers.\n\nDistributing the training across both GPUs to leverage the computing power available."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 4e20c2fc-6183-4f40-a309-33f7e6e1cded
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Distributing the training across both GPUs to leverage the computing power available.\n\nYou can find more information on these techniques and how to use them in HuggingFace\u2019s documentation, particularly in the \u201cEfficient Training on a Single GPU\u201d and \u201cEfficient Training on Multiple GPUs\u201d sections, as well as in \ud83e\udd17 Accelerate\u2019s documentation.\n\n5. Evaluation\n\nApart from training the model, we wanted to evaluate what it was doing and how well it was performing."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "5. Evaluation\n\nApart from training the model, we wanted to evaluate what it was doing and how well it was performing.\n\nFirstly, to correctly evaluate the training, we had to take a portion of the data to use as a validation split. Thankfully SQuAD2.0 and CoQA datasets already have a validation split. In the case of our dataset, we split our data into 85% training and 15% validation.\n\nTo assess its performance, we used the F1 Score by validating how many tokens appeared in common in both predictions and ground truth samples. We also used Exact match to see if the model was actually writing the same answer."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Since we had two different training steps, we also had two additional evaluation steps. The first training, on SQuAD2.0 and CoQa, resulted in a 74.29 F1 Score on the validation split after 3 epochs. The second training, on TryoCoQa, produced a 54.77 F1 Score after 166 epochs."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "More than analyzing the quantitative metrics is required to evaluate these results and conversational models in general. It is essential to consider the qualitative aspect of the model's answers, like their grammatical correctness and coherence within the conversation context. Sometimes it is preferable to have better answers (qualitatively speaking) than a better F1. So we looked at some answers from the validation set to ensure that the model was correctly generating what we were looking for. Our analysis revealed that higher F1 scores were generally associated with greater-quality answers. As a result, we selected the checkpoint with the highest F1 score to use in constructing our"}


created_at: 2024-05-14 14:30:36
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "were generally associated with greater-quality answers. As a result, we selected the checkpoint with the highest F1 score to use in constructing our demonstration chatbot."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "If you want to play around with our fine-tuned model, you can find it on \ud83e\udd17 Hugging Face with the ID tryolabs/long-t5-tglobal-base-blogpost-cqa!\n\nFaster inference with \ud83e\udd17 Optimum and ONNX"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 7467daa8-5fc4-4504-9e76-7effd2b391c7
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Faster inference with \ud83e\udd17 Optimum and ONNX\n\nAfter fine-tuning our model, we wanted to make it available to our awesome readers, so we deployed it on \ud83e\udd17 Hugging Face Spaces, which offers a free tier with two CPU cores for running inference on the model. However, this setup can lead to slow inference times, and processing significant inputs like ours doesn\u2019t make it any better. And a chatbot that takes a few minutes to answer a question doesn't strike anyone as being particularly chatty, does it? So, to improve the speed of our chatbot, we turned to \ud83e\udd17 Optimum and the ONNX Runtime!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: dd5416fe-b016-4920-a468-dd0b14667ce3
encoding: FLOAT32
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "In our previous blog post, A guide to optimizing Transformer-based models for faster inference, we used \ud83e\udd17 Optimum and ONNX to achieve an 8x speed-up on inference for a Transformer model. Be sure to check it out!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 6ecd7767-8db7-4ca3-8cda-3dfe88feb959
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Using \ud83e\udd17 Optimum\u2019s recently released exporters feature, we were able to convert our PyTorch model to the ONNX format. This feature is handy for encoder-decoder models like the LongT5 model we trained, as it exports the three main components separately: the encoder, the decoder with the Language Modeling head, and the same decoder with pre-computed hidden states as additional inputs. According to \ud83e\udd17 Optimum\u2019s documentation, combining these three components can speed up sequential decoding, which results in faster text generation.\n\nOur fine-tuned model, exported to ONNX into these three components, is also available on \ud83e\udd17 Hugging Face with the ID tryolabs/long-t5-tglobal-base-blogpost-cqa-onnx!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 15e35962-bd32-4e8b-a5d5-bf5fb1f119bf
encoding: FLOAT32
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Once our model was exported to ONNX, we used \ud83e\udd17 Optimum\u2019s integration with the ONNX Runtime to optimize our model and run inference on it by using the ORTModelForSeq2SeqLM class. This class can optimize and downcast the model using ONNX\u2019s tools and then use ONNX Runtime to run inference with this new, faster model! You can even take it one step further and quantize the model for even shorter inference time on CPU and lower memory consumption.\n\nWith these improvements, we achieved a 2x speed-up on inference time! Although the model still takes between 10 to 20 seconds to answer, this is a reasonable speed for a CPU-only deployment and processing such large inputs."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: d36b3983-2698-46bd-bf01-cbd09335968c
encoding: FLOAT32
metadata:
{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Average inference time measured over various blogposts.\n\nIt\u2019s worth noting that members of the ONNX community are actively working on tools for exporting and optimizing the latest ML architectures. The features mentioned in this section are fresh from the oven, thanks to the invaluable support of the ONNX team. Their work is crucial for unifying deep learning frameworks and optimizing models for faster and more cost-effective training and inference.\n\nImprovements\n\nWe\u2019re just scratching the surface of what\u2019s possible. There are numerous potential improvements to keep working on to enhance the user's overall experience and the chatbot's performance. Here are some ideas to keep working on:"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 4e71613b-d407-4748-a77e-af3164282ddc
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Fine-tune on more public datasets for different downstream tasks. By doing so, we hope that the model learns to respond in a more human-like manner. For example, we could fine-tune on context-free QA datasets or on free-form answers so that the model not only retrieves the answers from the context but also generates original answers. We could also fine-tune on summarizing or paraphrasing tasks so that the user can also ask the chatbot to rewrite certain parts of the blog or summarize entire sections!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "By doing so, we hope that the model learns to respond in a more human-like manner. For example, we could fine-tune on context-free QA datasets or on free-form answers so that the model not only retrieves the answers from the context but also generates original answers. We could also fine-tune on summarizing or paraphrasing tasks so that the user can also ask the chatbot to rewrite certain parts of the blog or summarize entire sections! Increase the size of our dataset. We built a small dataset for this demo, so it's not surprising that the model doesn't generalize well to new unseen blogs. We could potentially train a more accurate model by providing it with more training data."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "We built a small dataset for this demo, so it's not surprising that the model doesn't generalize well to new unseen blogs. We could potentially train a more accurate model by providing it with more training data. Optimize training using ONNX Runtime. We could achieve better results in the same amount of time if we could train for more epochs or fit a larger version of LongT5 in the same amount of memory. To do this, we could leverage the optimization power of ONNX Runtime for training PyTorch models, which would allow us to accelerate training speeds and optimize memory usage. Thanks to the recently released \ud83e\udd17 Optimum integration with ONNX Runtime for training, this can be done in just a"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "speeds and optimize memory usage. Thanks to the recently released \ud83e\udd17 Optimum integration with ONNX Runtime for training, this can be done in just a few lines of code!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: 5872bc45-1532-472e-ba31-fbf7af679845
encoding: FLOAT32
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "Takeaways\n\nWith the ever-increasing popularity of LLMs, it can seem almost impossible to train these models without having access to millions of dollars in resources and tons of data. However, with the right skills and knowledge about Foundation Models, Deep Learning, and the Transformer architecture, we showed you that fine-tuning these huge models is possible, even with few resources and a small dataset!\n\nFine-tuning is the key to unlocking the full potential of Foundation Models for your business. It allows you to take a pre-trained model and adapt it to your specific needs without breaking the bank."}


created_at: 2024-05-14 14:30:36
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{"title": "Fine-tuned LLMs and Distributed Training to elevate your Conversational AI game", "link": "https://tryolabs.com/blog/2023/01/27/fine-tuned-llms-and-distributed-training-to-elevate-your-conversational-ai-game", "language": "en", "description": "Unlock the power of Large Language Models (LLMs) in this step-by-step guide to fine-tuning for specific tasks. From building a custom dataset to optimizing inference time, we'll walk you through the process of fine-tuning LLMs like GPT-3, OPT, and T5 for NLP tasks.", "chroma:document": "And the best part? We're here to help you every step of the way. If you're ready to see how fine-tuning can benefit your business, don't hesitate to reach out, and let's get started on your fine-tuning journey today!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "Welcome to the 8th edition of our Top Python Libraries list!\n\nWe are excited to present this year's picks for the most innovative developments in the Python ecosystem. From this edition, we are expanding our list to include not only libraries per-se, but also tools that are built to belong in the Python ecosystem \u2014 some of which are not written in Python as you\u2019ll see.\n\n2021, 2020, 2019, 2018, 2017, 2016, 2015. See the other posts in this series: 2023"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "2021, 2020, 2019, 2018, 2017, 2016, 2015. See the other posts in this series: 2023\n\nThe rules for selection are the same as in previous years. We are looking for libraries that were launched or gained popularity in the past year, are well-maintained, and are simply cool and worth checking out. Our picks are heavily influenced by AI and data science libraries, but we also include a number of libraries that can be useful for non-data science purposes.\n\nIn addition to our main picks, we have also included an \"Extra picks\" section with a longer tail of libraries that we are sure many of you will find interesting."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "We hope that this list will showcase not only the best of what the Python community has to offer but also spark discussion about the libraries and tools that we may have missed. And unavoidably we do miss some every year, that's why we add the Misses from 2021 section as well.\n\nWithout further ado, let's get started!\n\nThe 10 main picks\n\n1. Ruff \u2014 a fast linter\n\nWas there anyone who did not use a linter in 2022?"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "Without further ado, let's get started!\n\nThe 10 main picks\n\n1. Ruff \u2014 a fast linter\n\nWas there anyone who did not use a linter in 2022?\n\nThrough the years, the community has come to acknowledge that linters are an essential part of the software development process. They analyze source code for potential errors and style issues, providing valuable feedback and suggestions for improvement. Ultimately, they help developers write cleaner, more efficient code. In order to make the most of this process, it is important to have a linter that is fast and effective. This is where Ruff comes in."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "Ruff is an extremely fast Python linter that is written in Rust. It is 10-100x faster than existing linters and can be installed via pip.\n\nLinting the CPython codebase from scratch.\n\nIn addition to linting, Ruff can also be used as an advanced code transformation tool, capable of upgrading type annotations, rewriting class definitions, sorting imports, and more.\n\nIt is a powerful tool that aims to replace a variety of other tools, including Flake8, isort, pydocstyle, yesqa, eradicate, and even a subset of pyupgrade and autoflake, all while executing at lightning fast speeds.\n\nDefinitely a big highlight to add to your arsenal going into 2023!\n\n2. python-benedict \u2014 a dict on steroids"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "Definitely a big highlight to add to your arsenal going into 2023!\n\n2. python-benedict \u2014 a dict on steroids\n\nDictionaries are a vital data structure in Python, but working with complex dictionaries can be a challenge. The built-in dict type is powerful, but it lacks many features that make it easier to access and manipulate nested values or convert dictionaries to and from different data formats. If you've found yourself struggling to work with dictionaries in Python, python-benedict could be the solution you've been looking for."}


created_at: 2024-05-14 14:30:36
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "benedict is a subclass of the built-in dict type, meaning that it is fully compatible with existing dictionaries and can be used as a drop-in replacement in most cases.\n\nOne of the key features of benedict is its support for keylists and keypaths. This makes it easy to access and manipulate values in complex dictionaries without having to manually dig through nested levels. For example:"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "In addition to its keylist and keypath support, benedict also provides a wide range of I/O shortcuts for working with various data formats. You can easily read and write dictionaries to and from formats such as JSON, YAML, and INI, as well as more specialized formats like CSV, TOML, and XML. It also has support for multiple I/O operations backends such as filepath (read/write), url (read-only),and s3 (read/write).\n\nIt has a gazillion more features, so make sure to check out the readme.\n\n3. Memray \u2014 a memory profiler"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "It has a gazillion more features, so make sure to check out the readme.\n\n3. Memray \u2014 a memory profiler\n\nOptimizing the memory usage of your systems is vital for improving their performance and stability. Memory leaks can cause programs to consume increasing amounts of memory, degrade overall system performance and eventually crash. Although garbage-collected and generally easy to work with, Python does not shield you from these issues. Nothing prevents circular references or unreachable objects (for more on this, read on how garbage collection works); even more so if we talk about C/C++ extensions."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "A memory profiler can help you identify and fix these issues, making it an essential tool for optimizing the memory usage of your programs. This is where Memray comes in handy!\n\nIt is a memory profiler that tracks memory allocations in Python code, native extension modules, and the Python interpreter itself, providing a comprehensive view of memory usage. Memray generates various reports, including flame graphs, to help you analyze the collected data and identify issues such as leaks and hotspots. It is fast and works with Python and native threads, making it a versatile tool for debugging memory issues in multithreaded programs.\n\nMemray live mode."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "Memray live mode.\n\nMemray can be used as a command line tool or as a library for more fine-grained profiling tasks. Its live mode allows you to interactively inspect memory usage while a script or module is running, making it useful for debugging complex memory patterns.\n\nA must-have tool by Bloomberg for the serious Python programmer.\n\n4. Codon \u2014 a Python compiler using LLVM"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "A must-have tool by Bloomberg for the serious Python programmer.\n\n4. Codon \u2014 a Python compiler using LLVM\n\nWe all love Python for its simplicity and ease of use, but sometimes we need a little extra runtime speed. Even with all the optimizations, Python interpreters (like CPython) can only go so far. When performance needs to be pushed even further, compilers come in. They convert our Python code into machine code that can be directly executed by the processor, skipping the interpreter step and giving us some serious performance boosts."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "Codon is a high-performing ahead-of-time Python compiler that can even compete with C/C++ in terms of speed, with typical speedups reported to be 10-100x or more (on a single thread). It can be used within a larger Python codebase using the @codon.jit decorator or by calling plain Python functions and libraries from within Codon using Python interoperability."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "There is no free lunch here, so you will likely have to make some modifications to your Python code so that it can be compiled by Codon. But the restrictions that Codon imposes over the language are what ultimately allow for the performance gains. Nevertheless, the compiler will guide you by producing detailed error messages that identify and help you resolve incompatibilities.\n\nDiagram of how Codon works.\n\nIt's definitely worth checking out if you're looking to speed up your code. The FAQ is very informative for getting a quick glance at what this tool will do for you.\n\n5. \ud83e\udd9c\ufe0f\ud83d\udd17 LangChain \u2014 building LLM-powered apps"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "5. \ud83e\udd9c\ufe0f\ud83d\udd17 LangChain \u2014 building LLM-powered apps\n\nUnless you have been living under a rock, you are well aware that generative AI has been taking the world by storm this year. A large chunk of this is Large Language Models (LLMs).\n\nA lot of code that we have written over these past years to solve AI problems can be just thrown away and replaced with a LLM (like GPT-3 or its evolutions \u2014 InstructGPT or ChatGPT \u2014 T5, or any other). Also, we have witnessed the birth of a new programming language for the LLM interface: text-based prompts.\n\nLangChain comes in to help leverage the full power of LLMs."}


created_at: 2024-05-14 14:30:36
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "LangChain comes in to help leverage the full power of LLMs.\n\nFirst: in any serious use of LLM, one does not typically need to think of prompts as a one-off thing but rather a combination of things: templates, user input, and input/output examples that the LLM can take as reference. LangChain helps you streamline this \u201cprompt management\u201d by providing interfaces for building different prompts based on the individual components straightforwardly."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "Second, for building the prompts, sometimes you need to inject external knowledge (or even other models). For example, imagine you need to perform a database query to extract a customer\u2019s name for a personalized email. This is the concept of chains, and LangChain provides a unified interface for this.\n\nThen there\u2019s the concept of fetching and augmenting data so the LLM can work over your own data, contrary to the \u201cgeneric\u201d data over which the models are trained.\n\nThere are more things you can do with LangChain, such as being prepared to switch to another model/provider without changing your code, building agents with memory, etc."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "Certainly, an innovative tool that we expect will grow a lot in 2023!\n\n6. fugue \u2014 distributed computing done easy\n\nIf you're familiar with Pandas or SQL, you know that these tools are great for working with small to medium-sized datasets. But when dealing with larger amounts of data, you often need a distributed computing framework like Spark to process it efficiently. The thing is, Spark is a whole different beast from Pandas or SQL. The syntax and concepts are completely different, and it can be a challenge to migrate your code from one to the other. That's where Fugue comes in."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "Fugue is a library that makes it easier to work with distributed computing frameworks like Spark, Dask, and Ray. It provides a unified interface for distributed computing that lets you execute Python, pandas, and SQL code on Spark, Dask, and Ray with minimal rewrites.\n\nThe best starting point is using Fugue\u2019s transform() function. It lets you parallelize the execution of a single function by bringing it to Spark, Dask, or Ray. See the following example where the map_letter_to_food() function is brought to the Spark execution engine:"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "Fugue allows you to maintain one single codebase for Spark, Dask and Ray projects where logic and execution are completely decoupled, saving programmers from having to learn each of the different frameworks.\n\nThe library also has several other interesting features and a growing community, so make sure to check it out!\n\n7. Diffusers \u2014 generative AI"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "7. Diffusers \u2014 generative AI\n\n2022 will always be remembered as the year when generative AI breached the frontiers of the AI community and expanded to the outside world. This was largely powered by Diffusion models, which gained much attention thanks to their impressive capabilities in generating high-quality images. DALL\u00b7E 2, Imagen, and Stable Diffusion are just a few examples of diffusion models that have made waves this year. Their results inspired discussion and admiration for their ability to generate images that push the boundaries of what was previously thought possible \u2014 even by AI experts."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "Hugging Face's Diffusers library is a collection of tools and techniques for working with diffusion models, including the Stable Diffusion model, which has proven particularly effective at generating highly realistic and detailed images. The library also includes tools for optimizing the performance of image-generation models and analyzing the results of image-generation experiments.\n\nStarting to use this library for text-to-image generation may be as simple as dropping these few lines of code:\n\nBut Diffusers doesn\u2019t stop with images. Features for audio generation and even molecule generation (!) are coming soon."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "But Diffusers doesn\u2019t stop with images. Features for audio generation and even molecule generation (!) are coming soon.\n\nIt looks like most open-source implementations of Diffusion models will leverage the building blocks provided by this library. Sweet!\n\n8. LineaPy \u2014 convert notebooks to production pipelines"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "8. LineaPy \u2014 convert notebooks to production pipelines\n\nJupyter notebooks are an extremely useful tool for prototyping and developing code. They allow you to easily mix code, plots, media, and interactive widgets in a single document, making it easy to document and understand your code as you develop it. They are the perfect playground for experimenting with code and testing out ideas, which can be especially handy for data analysis and machine learning tasks. But as the ideas progress and one is happy with the result and wants to put code into production, challenges start to show."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "Code within notebooks may not always be structured in a way that is easy to deploy to a production environment. Should you take the notebook\u2019s code and rewrite it elsewhere, modularize it and make it follow the best practices? This is the most common approach, but the drawback is that if, later down the road, you want to improve or debug something, you lost the interactivity of the notebook. So now you have to maintain both a notebook and separate production code, which is far from ideal.\n\nThere is a path to do better, thanks to LineaPy."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "There is a path to do better, thanks to LineaPy.\n\nLineaPy is a Python library that helps you move quickly from prototyping to creating robust data pipelines. It takes your messy notebook and helps clean up and refactor code, making it easier to run in an orchestration system or job schedulers, such as cron, Apache Airflow, or Prefect.\n\nIt also helps with reproducibility: it has the concept of \u201cartifacts\u201d that encapsulate both data and code that can help you trace back how values were produced. At a high level, LineaPy traces the sequence of code execution to form a comprehensive understanding of the code and its context."}


created_at: 2024-05-14 14:30:36
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "The best part? It\u2019s very simple to integrate, and can run in a Notebook environment with just two lines of code!\n\nMake sure to check out the documentation, which does a great job showcasing the problems the tool solves with very comprehensive examples.\n\n9. whylogs \u2014 model monitoring\n\nAs AI models make their way to produce actual value for a business, their behavior in production must be monitored continuously to make sure that the value is sustained over time. That is, one must have a way to tell that model predictions are reliable and accurate and that the inputs that are being fed to the model do not deviate significantly from the type of data that was used to train it."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "But model monitoring is not just limited to AI models \u2014 it can be applied to any kind of model, including statistical and mathematical models. Hence the usefulness of this pick.\n\nwhylogs is an open-source library that enables you to log and analyze any kind of data. It provides a range of features, starting with the ability to generate summaries of datasets: whylogs profiles."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "The profiles capture statistics over the original data, such as distribution, missing values, and many other configurable metrics. They are computed locally using the library and can even be merged to allow for analysis over distributed and streaming systems. They form a representation of the data that conveniently does not need to expose the data itself, but only metrics derived from it \u2014 which is good for privacy.\n\nProfiles are straightforward to generate. For example, to generate a profile from a Pandas DataFrame you would do:"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "Profiles are straightforward to generate. For example, to generate a profile from a Pandas DataFrame you would do:\n\nBut profiles are only as useful as what you decide to do with them. For this, visualization is a must. You can install the viz module using pip install \"whylogs[viz]\" which can create interactive reports for you. Here\u2019s a sample drift report inside a Jupyter Notebook:\n\nSample drift report using whylogs viz module.\n\nYou can also set constraints so that you are notified when data doesn\u2019t match the expectations, effectively doing some kind of testing on your data/model. After all, isn\u2019t this what we were set to do from the beginning?"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "If you prefer managed solutions (remember you do not need to share your data, but only statistics!), you can opt for the parent company\u2019s SaaS ML monitoring and AI observability platform, which will make setting all this up very easy and also has a free starter edition. Nice!\n\n10. Mito \u2014 spreadsheet inside notebooks\n\nIn the era of data science, many people are transitioning from analyzing data manually in spreadsheets to wring code to do that. But there\u2019s no denying that spreadsheets are an attractive tool, providing a streamlined interface for editing and immediate feedback that allows for fast iterations."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "Many times, we process data using spreadsheets. But if we want to do the same thing again over new data, we must start from scratch! Code is so much better at this, saving us precious time.\n\nCould we get the best of both worlds? Meet Mito.\n\nMito comes in handy as it is a library that allows you to work with data in a spreadsheet-like interface within Jupyter Notebooks. It allows you to import and edit CSV and XLSX files, generate pivot tables and graphs, filter and sort data, merge data sets, and perform a variety of other data manipulation tasks.\n\nAnd the most important feature: Mito will generate Python code that corresponds to each of your edits!\n\nHow Mito works."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "And the most important feature: Mito will generate Python code that corresponds to each of your edits!\n\nHow Mito works.\n\nMito also supports the use of Excel-style formulas and provides summary statistics for columns of data. It\u2019s designed to be the first tool in the data science toolkit, meaning it\u2019s built to be a user-friendly tool for data exploration and analysis.\n\nTools like Mito lower the barrier of entry to the data science world, allowing people familiar with software such as Excel or Google Sheets (nearly everyone?) to start contributing code fast, and therefore deserves a spot in our top 10 picks this year.\n\nExtra picks aka the long tail \u2014 don\u2019t miss these\n\nNon-AI"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "Extra picks aka the long tail \u2014 don\u2019t miss these\n\nNon-AI\n\nNeutron \u2014 native Python apps along with CSS and HTML for frontend design.\n\nCustomTkinter \u2014 modern and customizable Python UI-library based on Tkinter.\n\nPyxel \u2014 retro game engine for Python.\n\nFeathr \u2014 an enterprise-grade, high-performance feature store for your AI models. By LinkedIn.\n\nHugie \u2014 CLI interface for working with Hugging Face\u2019s Inference Endpoints API.\n\nInvokeAI \u2014 a Stable Diffusion toolkit, fork of the original but with several more features like an interactive Web UI.\n\nKangas \u2014 exploring, analyzing, and visualizing large-scale multimedia data."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "Kangas \u2014 exploring, analyzing, and visualizing large-scale multimedia data.\n\nLance \u2014 alternative to Parquet. 100x faster for random access, automatic versioning, optimized for ML data. Apache Arrow and DuckDB compatible.\n\nMarqo \u2014 an open-source tensor search engine that seamlessly integrates with your applications, websites, and workflow.\n\nMercury \u2014 convert Jupyter Notebooks to web apps and share with non-technical users.\n\nMLEM \u2014 a tool to package, serve, and deploy any ML model on any platform. By Iterative AI (creators of DVC).\n\nStable Diffusion web UI - browser interface based on Gradio library for Stable Diffusion, with a lot of features.\n\nLLMs and the CLI"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "Stable Diffusion web UI - browser interface based on Gradio library for Stable Diffusion, with a lot of features.\n\nLLMs and the CLI\n\nAll of these are handy Python tools to basically ask GPT-3 (or its variants) to output commands for the shell, based on human-language prompts:\n\nTorch-X\n\nTorchMultimodal \u2014 PyTorch domain library for training SoTA multi-task multimodal models at scale. Modular and composable building blocks plus examples. See launch blog post. By Meta.\n\nTorchRec \u2014 for recommendation systems, providing common sparsity and parallelism primitives, enabling researchers to build state-of-the-art personalization models and deploy them in production. See launch blog post. By Meta."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "TorchScale \u2014 allow researchers and developers to scale up Transformers efficiently and effectively.\n\nTorchView \u2014 visualize PyTorch models as graphs using Graphviz.\n\nComputer Vision\n\npybboxes \u2014 convert between different formats of bounding boxes for computer vision applications.\n\nsuper-gradients \u2014 easily train or fine-tune SOTA computer vision models with one open source training library. By Deci.ai.\n\nvideo-transformers \u2014 fine-tuning models for video classification.\n\nNLP\n\nLingua \u2014 accurate language detection for +75 languages both in long and short texts."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "NLP\n\nLingua \u2014 accurate language detection for +75 languages both in long and short texts.\n\nPyChatGPT \u2014 Python client for the unofficial ChatGPT API, which doesn\u2019t require a headless browser. This isn\u2019t official and is bypassing the ever-evolving protections of the OpenAI site, so you can call ChatGPT programmatically. Use it while it works; it might break soon!\n\nSetFit \u2014 an efficient and prompt-free framework for few-shot fine-tuning of Sentence Transformers.\n\nDistributed / large model training / scale\n\nColossalAI \u2014 efficient distributed learning.\n\nDeepSpeed \u2014 a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. By Microsoft."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "DeepSpeed \u2014 a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. By Microsoft.\n\nvoltaML \u2014 open-source lightweight library to accelerate your machine learning and deep learning models. Optimize, compile and deploy your models to your target CPU and GPU devices, with just one line of code.\n\nInterpretability\n\nFastTreeSHAP \u2014 fast implementation of the TreeSHAP algorithm in the SHAP package, for interpreting tree-based models. Blog post.\n\nsurgeon-pytorch \u2014 inspect and extract intermediate layers of PyTorch models.\n\nOthers"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "surgeon-pytorch \u2014 inspect and extract intermediate layers of PyTorch models.\n\nOthers\n\nanomalib \u2014 anomaly detection library with SOTA algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.\n\nbayesian-statistics-toolbox \u2014 a library of functions for running sophisticated Bayesian analyses in a simple, straight forward manner.\n\nopen-metric-learning \u2014 a PyTorch-based framework to train and validate the models producing high-quality embeddings.\n\nponiard \u2014 find out the best scikit-learn model by fitting them all and comparing them (not AutoML!).\n\nMisses from 2021 (sorry!)\n\nArgilla \u2014 data-centric NLP, build datasets in less time."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "Misses from 2021 (sorry!)\n\nArgilla \u2014 data-centric NLP, build datasets in less time.\n\nAutoViz \u2014 visualize any dataset, any size with a single line of code.\n\nContextualized Topic Models (CTM) \u2014 use pre-trained representations of language (like BERT) to support topic modeling.\n\nFairScale \u2014 large-scale distributed training on PyTorch.\n\nFiftyOne \u2014 open-source tool for building high-quality datasets and computer vision models. Get hands-on with your data, visualize labels, evaluate models, explore, find annotation mistakes, and more.\n\nIvy \u2014 attempt to unify all ML frameworks. Currently supports JAX, TensorFlow, PyTorch, and Numpy. Soon, automatic code conversions and multi-framework support."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "Jury \u2014 evaluate performance of NLP models (see blog post).\n\nPloomber \u2014 fast way to build data pipelines and deploy anywhere.\n\nShapash \u2014 make machine learning interpretable and understandable by everyone. Live demo.\n\nSmall-Text \u2014 state-of-the-art active learning for text classification.\n\nWeaSEL \u2014 based on the End-to-End Weak Supervision paper (NeurIPS 2021), allows you to train your favorite neural network for weakly-supervised classification."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "Top Python libraries of 2022", "link": "https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022", "language": "en", "description": "There are so many amazing Python libraries and tools out every year that it's hard to keep track of them all. That's why we share with you our hand-picked selection of our best picks.", "chroma:document": "WeightWatcher \u2014 predict performance of neural networks without access to training or test data, solely based on theory and statistical properties of the intermediate layers. Original version from earlier than 2021, but it\u2019s getting a lot of development and traction this year!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "A guide to optimizing Transformer-based models for faster inference", "link": "https://tryolabs.com/blog/2022/11/24/transformer-based-model-for-faster-inference", "language": "en", "description": "Learn how to optimize your Transformer-based model for faster inference in this comprehensive guide that covers techniques for reducing the size and time required for execution.", "chroma:document": "Have you ever suffered from high inference time when working with Transformers? In this blog post, we will show you how to optimize and deploy your model to improve speed up to x10!\n\nIf you have been keeping up with the fast-moving world of AI, you surely know that in recent years Transformer models have taken over the state-of-the-art in many vital tasks on NLP, Computer Vision, time series analysis, and much more."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "A guide to optimizing Transformer-based models for faster inference", "link": "https://tryolabs.com/blog/2022/11/24/transformer-based-model-for-faster-inference", "language": "en", "description": "Learn how to optimize your Transformer-based model for faster inference in this comprehensive guide that covers techniques for reducing the size and time required for execution.", "chroma:document": "Although ubiquitous, many of these models are very big and slow at inference time, requiring billions of operations to process inputs. Ultimately, this affects user experience and increases infrastructure costs as heaps of memory and computing power are needed to run these models. Even our planet is affected by this, as more computing power demands more energy, which could translate to more pollution."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "A guide to optimizing Transformer-based models for faster inference", "link": "https://tryolabs.com/blog/2022/11/24/transformer-based-model-for-faster-inference", "language": "en", "description": "Learn how to optimize your Transformer-based model for faster inference in this comprehensive guide that covers techniques for reducing the size and time required for execution.", "chroma:document": "One solution for accelerating these models can be buying more powerful hardware. However, this isn\u2019t a very scalable solution. Also, it may be the case that we can\u2019t just use bigger hardware as we may want to run these models on the edge: mobile phones, security cameras, vacuum cleaners, or self-driving vehicles. By nature, these embedded devices are resource-constrained, with limited memory, computing power, and battery life. So, how can we make the most out of our hardware, whether a mobile phone or a huge data center? How can we fit our model in less memory, maintaining accuracy? And how can we make it faster or even run in real time?"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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{"title": "A guide to optimizing Transformer-based models for faster inference", "link": "https://tryolabs.com/blog/2022/11/24/transformer-based-model-for-faster-inference", "language": "en", "description": "Learn how to optimize your Transformer-based model for faster inference in this comprehensive guide that covers techniques for reducing the size and time required for execution.", "chroma:document": "The answer to these questions is optimization. This blog post presents a step-by-step guide with overviews of various techniques, plus a notebook hosted on Google Colab so you can easily reproduce it on your own!\n\nOur code is available here.\n\nYou can apply many optimization techniques to make your models much more efficient: graph optimization, downcasting and quantization, using specialized runtimes, and more. Unfortunately, we couldn\u2019t find many comprehensive guides on how to apply these techniques to Transformer models."}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
id: cdf145fe-5d91-4a82-ae64-d129cb23cd5f
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{"title": "A guide to optimizing Transformer-based models for faster inference", "link": "https://tryolabs.com/blog/2022/11/24/transformer-based-model-for-faster-inference", "language": "en", "description": "Learn how to optimize your Transformer-based model for faster inference in this comprehensive guide that covers techniques for reducing the size and time required for execution.", "chroma:document": "We take a fine-tuned version of the well-known BERT model architecture as a case study. You will see how to perform model optimization by loading the transformer model, pruning the model with sparsity and block movement, converting the model to ONNX, and applying graph optimization and downcasting. Moreover, we will show you how to deploy your optimized model on an inference server.\n\nTry it out\n\nWe prepared and included the following live frontend on a \ud83e\udd17 Hugging Face space so you can test and compare the baseline model, which is a large BERT fine-tuned on the squadv2 dataset, with an already available pruned version of this baseline, and finally, our \u2018pruned + optimized\u2019 version!"}


created_at: 2024-05-14 14:30:36
topic: persistent://default/default/3c4faa6d-6b2b-430c-8727-e0594beeca70
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encoding: FLOAT32
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{"title": "A guide to optimizing Transformer-based models for faster inference", "link": "https://tryolabs.com/blog/2022/11/24/transformer-based-model-for-faster-inference", "language": "en", "description": "Learn how to optimize your Transformer-based model for faster inference in this comprehensive guide that covers techniques for reducing the size and time required for execution.", "chroma:document": "In the interactive demo below, you will see three fields:\n\nthe Model field, where you can select any of the three models mentioned before (by default, our optimized model).\n\nfield, where you can select any of the three models mentioned before (by default, our optimized model). the Text field, where you should provide context about a topic.\n\nfield, where you should provide context about a topic. the Question field, where you can ask the AI about something in the text.\n\nYou can also try the demo here.\n\nQuestion Answering (QA) models solve a very simple task: retrieve the answer to questions about a given text.\n\nLet\u2019s take the following text as an example:"}


