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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts. Human oversight : Including human involvement in AI decision-making processes.

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Governing ML lifecycle at scale: Best practices to set up cost and usage visibility of ML workloads in multi-account environments

AWS Machine Learning

For a multi-account environment, you can track costs at an AWS account level to associate expenses. A combination of an AWS account and tags provides the best results. Tagging is an effective scaling mechanism for implementing cloud management and governance strategies.

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Govern generative AI in the enterprise with Amazon SageMaker Canvas

AWS Machine Learning

This is crucial for compliance, security, and governance. In this post, we analyze strategies for governing access to Amazon Bedrock and SageMaker JumpStart models from within SageMaker Canvas using AWS Identity and Access Management (IAM) policies. We provide code examples tailored to common enterprise governance scenarios.

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Use Amazon SageMaker Model Card sharing to improve model governance

AWS Machine Learning

The framework that gives systematic visibility into ML model development, validation, and usage is called ML governance. During AWS re:Invent 2022, AWS introduced new ML governance tools for Amazon SageMaker which simplifies access control and enhances transparency over your ML projects.

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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

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Use the ApplyGuardrail API with long-context inputs and streaming outputs in Amazon Bedrock

AWS Machine Learning

The new ApplyGuardrail API enables you to assess any text using your preconfigured guardrails in Amazon Bedrock, without invoking the FMs. In this post, we demonstrate how to use the ApplyGuardrail API with long-context inputs and streaming outputs. For example, you can now use the API with models hosted on Amazon SageMaker.

APIs 133
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Secure Amazon SageMaker Studio presigned URLs Part 3: Multi-account private API access to Studio

AWS Machine Learning

These customers need to balance governance, security, and compliance against the need for machine learning (ML) teams to quickly access their data science environments in a secure manner. One important aspect of this foundation is to organize their AWS environment following a multi-account strategy.

APIs 92