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:, where you can find the SDK source and installation instructions for the library.


The SageMaker Python SDK supports managed training and inference for a variety of machine learning frameworks:

SageMaker Built-in Algorithms

Amazon SageMaker provides implementations of some common machine learning algorithms optimized for GPU architecture and massive datasets.


Orchestrate your SageMaker training and inference workflows with Airflow and Kubernetes.

Amazon SageMaker Experiments

You can use Amazon SageMaker Experiments to track machine learning experiments.

Amazon SageMaker Feature Store

You can use Feature Store to store features and associated metadata, so features can be discovered and reused.

Amazon SageMaker Model Monitoring

You can use Amazon SageMaker Model Monitoring to automatically detect concept drift by monitoring your machine learning models.

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

Amazon SageMaker Model Building Pipeline

You can use Amazon SageMaker Model Building Pipelines to orchestrate your machine learning workflow.