Amazon SageMaker is a cloud machine-learning platform that was launched in November 2017. SageMaker enables developers to create, train, and deploy machine-learning (ML) models in the cloud. SageMaker also enables developers to deploy ML models on embedded systems and edge-devices. Capabilities SageMaker enables developers to operate at a number of levels of abstraction when training and deploying machine learning models. At its highest level of abstraction, SageMaker provides pre-trained ML models that can be deployed as-is. In addition, SageMaker provides a number of built-in ML algorithms that developers can train on their own data. Further, SageMaker provides managed instances of TensorFlow and Apache MXNet, where developers can create their own ML algorithms from scratch. Regardless of which level of abstraction is used, a developer can connect their SageMaker-enabled ML models to other AWS services, such as the Amazon DynamoDB database for structured data storage, AWS Batch for offline batch processing, or Amazon Kinesis for real-time processing. Development interfaces A number of interfaces are available for developers to interact with SageMaker. First, there is a web API that remotely controls a SageMaker server instance. While the web API is agnostic to the programming language used by the developer, Amazon provides SageMaker API bindings for a number of languages, including Python, JavaScript, Ruby, Java, and Go. In addition, SageMaker provides managed Jupyter Notebook instances for interactively programming SageMaker and other applications. History and features 2017-11-29: SageMaker is launched at the AWS re:Invent conference. 2018-02-27: Managed TensorFlow and MXNet deep neural network training and inference are now supported within SageMaker. 2018-02-28: SageMaker automatically scales model inference to multiple server instances. 2018-07-13: SageMaker adds support for recurrent neural network training, word2vec training, multi-class linear learner training, and distributed deep neural network training in Chainer with Layer-wise Adaptive Rate Scaling (LARS). 2018-07-17: AWS Batch Transform enables high-throughput non-realtime machine learning inference in SageMaker. 2018-11-08: Support for training and inference of Object2Vec word embeddings. 2018-11-27: SageMaker Ground Truth "makes it much easier for developers to label their data using human annotators through Mechanical Turk, third-party vendors, or their own employees." 2018-11-28: SageMaker Reinforcement Learning (RL) "enables developers and data scientists to quickly and easily develop reinforcement learning models at scale." 2018-11-28: SageMaker Neo enables deep neural network models to be deployed from SageMaker to edge-devices such as smartphones and smart cameras. 2018-11-29: The AWS Marketplace for SageMaker is launched. The AWS Marketplace enables 3rd-party developers to