AWS GovCloud (US) User Guide
AWS GovCloud (US) User Guide

Amazon SageMaker

Amazon SageMaker is a fully managed machine learning service. With Amazon SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so you don't have to manage servers. It also provides common machine learning algorithms that are optimized to run efficiently against extremely large data in a distributed environment. With native support for bring-your-own-algorithms and frameworks, Amazon SageMaker provides flexible distributed training options that adjust to your specific workflows.

This service has no differences between AWS GovCloud (US) Regions and the standard AWS Regions.

For more information about Amazon SageMaker, see the Amazon SageMaker documentation.

ITAR Boundary

AWS GovCloud (US) has an ITAR boundary, which defines where customers are allowed to store ITAR-controlled data for this service in AWS GovCloud (US) Regions. To maintain ITAR compliance, you must place ITAR-controlled data on the applicable part of the ITAR boundary. If you do not have any ITAR-controlled data in AWS GovCloud (US) Regions, this section does not apply to you. The following information identifies the ITAR boundary for this service:

ITAR-Regulated Data Permitted ITAR-Regulated Data Not Permitted
  • All data entered, stored, and processed within a Notebook Instance and ephemeral drives can contain ITAR-regulated data. All data processed during training, automatic model tuning, batch transformation, and endpoint invocation can contain ITAR-regulated data.

  • Amazon SageMaker metadata is not permitted to contain ITAR-regulated data. This metadata includes all configuration data that you enter when creating and maintaining your NotebookInstances, NotebookInstanceLifecycleConfigs, Endpoints, Models, EndpointConfigs, TrainingJobs, HyperParameterTuningJobs, and BatchTransformJobs.

    Do not enter ITAR-regulated data in the following console fields:

    • NotebookInstance Name

    • NotebookInstanceLifecycleConfig Name

    • Model Name

    • Model Container Hostname

    • Model Environment names and values

    • Endpoint Name

    • Endpoint Config Name

    • Endpoint Config Production Variant names

    • Endpoint Config

    • TrainingJob Name

    • BatchTransformJob Name

    • Hyperparameter Names or values

    • Input Channel Name

    • Any resource tag or value.

    • Names of any metrics emitted by algorithms.

    • Names of any training or inference container environment variables.

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