########################### Amazon SageMaker Python SDK ########################### Amazon SageMaker Python SDK is an open source library for training and deploying machine-learned models on Amazon SageMaker. With the SDK, you can train and deploy models using popular deep learning frameworks, algorithms provided by Amazon, or your own algorithms built into SageMaker-compatible Docker images. Here you'll find an overview and API documentation for SageMaker Python SDK. The project homepage is in Github: https://github.com/aws/sagemaker-python-sdk, where you can find the SDK source and installation instructions for the library. ******** Overview ******** .. toctree:: :maxdepth: 2 overview v2 The SageMaker Python SDK APIs: .. toctree:: :maxdepth: 2 api/index ********** Frameworks ********** The SageMaker Python SDK supports managed training and inference for a variety of machine learning frameworks: .. toctree:: :maxdepth: 2 frameworks/index ******************************** SageMaker Built-in Algorithms ******************************** Amazon SageMaker provides implementations of some common machine learning algorithms optimized for GPU architecture and massive datasets. .. toctree:: :maxdepth: 2 algorithms/index ************* Workflows ************* Orchestrate your SageMaker training and inference workflows with Airflow and Kubernetes. .. toctree:: :maxdepth: 2 workflows/index **************************** Amazon SageMaker Experiments **************************** You can use Amazon SageMaker Experiments to track machine learning experiments. .. toctree:: :maxdepth: 2 experiments/index ************************* Amazon SageMaker Debugger ************************* You can use Amazon SageMaker Debugger to automatically detect anomalies while training your machine learning models. .. toctree:: :maxdepth: 2 amazon_sagemaker_debugger ****************************** Amazon SageMaker Feature Store ****************************** You can use Feature Store to store features and associated metadata, so features can be discovered and reused. .. toctree:: :maxdepth: 2 amazon_sagemaker_featurestore ********************************* Amazon SageMaker Model Monitoring ********************************* You can use Amazon SageMaker Model Monitoring to automatically detect concept drift by monitoring your machine learning models. .. toctree:: :maxdepth: 2 amazon_sagemaker_model_monitoring *************************** Amazon SageMaker Processing *************************** You can use Amazon SageMaker Processing to perform data processing tasks such as data pre- and post-processing, feature engineering, data validation, and model evaluation .. toctree:: :maxdepth: 2 amazon_sagemaker_processing ***************************************** Amazon SageMaker Model Building Pipeline ***************************************** You can use Amazon SageMaker Model Building Pipelines to orchestrate your machine learning workflow. .. toctree:: :maxdepth: 2 amazon_sagemaker_model_building_pipeline