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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 | |