index.rst

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

overview v2

The SageMaker Python SDK APIs:

api/index

Frameworks

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

frameworks/index

SageMaker First-Party Algorithms

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

algorithms/index

Workflows

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

workflows/index

Amazon SageMaker Debugger

You can use Amazon SageMaker Debugger to automatically detect anomalies while training your machine learning models.

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Amazon SageMaker Feature Store

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

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Amazon SageMaker Model Monitoring

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

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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

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