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###########################
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 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