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Amazon SageMaker Python SDK
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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.
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Overview
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overview
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The SageMaker Python SDK APIs:
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api/index
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Frameworks
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The SageMaker Python SDK supports managed training and inference for a variety of machine learning frameworks:
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frameworks/index
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SageMaker Built-in Algorithms
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Amazon SageMaker provides implementations of some common machine learning algorithms optimized for GPU architecture and massive datasets.
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algorithms/index
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Workflows
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Orchestrate your SageMaker training and inference workflows with Airflow and Kubernetes.
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workflows/index
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Amazon SageMaker Debugger
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You can use Amazon SageMaker Debugger to automatically detect anomalies while training your machine learning models.
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amazon_sagemaker_debugger
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Amazon SageMaker Feature Store
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You can use Feature Store to store features and associated metadata, so features can be discovered and reused.
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amazon_sagemaker_featurestore
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Amazon SageMaker Model Monitoring
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You can use Amazon SageMaker Model Monitoring to automatically detect concept drift by monitoring your machine learning models.
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amazon_sagemaker_model_monitoring
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Amazon SageMaker Processing
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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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amazon_sagemaker_processing
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Amazon SageMaker Model Building Pipeline
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You can use Amazon SageMaker Model Building Pipelines to orchestrate your machine learning workflow.
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amazon_sagemaker_model_building_pipeline