Key components to consider when designing ML systems include: Problem Definition: Clearly define the problem, data requirements, and evaluation metrics. Success criteria often involve accuracy, latency, and scalability. Data Pipeline: Build automated pipelines to collect, clean, transform, and validate data. Model Selection and Training: Choose appropriate algorithms (e.g., linear regression, decision trees, neural networks) and train models using frameworks like TensorFlow or PyTorch. Deployment and Serving: Deploy trained models to production environments using scalable architectures such as containerized services (e.g., Docker and Kubernetes). Monitoring and Maintenance: Continuously monitor model performance, retrain as necessary, and ensure data drift is addressed.
Key components to consider when designing ML systems include: Problem Definition: Clearly define the problem, data requirements, and evaluation metrics. Success criteria often involve accuracy, latency, and scalability. Data Pipeline: Build automated pipelines to collect, clean, transform, and validate data. Model Selection and Training: Choose appropriate algorithms (e.g., linear regression, decision trees, neural :networks) and train models using frameworks like TensorFlow or PyTorch. Deployment and Serving: Deploy trained models to production environments using scalable architectures such as containerized services (e.g., Docker and Kubernetes). Monitoring and Maintenance Continuously monitor model performance, retrain as necessary, and ensure data drift is addressed.