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.

MXNet

A managed environment for MXNet training and hosting on Amazon SageMaker

Scikit-Learn

A managed enrionment for Scikit-learn training and hosting on Amazon SageMaker

PyTorch

A managed environment for PyTorch training and hosting on Amazon SageMaker

Chainer

A managed environment for Chainer training and hosting on Amazon SageMaker

Reinforcement Learning

A managed environment for Reinforcement Learning training and hosting on Amazon SageMaker

SparkML Serving

A managed environment for SparkML hosting on Amazon SageMaker

SageMaker First-Party Algorithms

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

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