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Amazon SageMaker MLflow sync governance

Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 2

官方一手AWS Machine Learning Blog@Melanie Li
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Developers managing ML models across multiple AWS accounts can now implement centralized governance or maintain development isolation.

Amazon SageMaker AI Model Registry now supports cross-account governance with two topologies. The hub-and-spoke pattern centralizes governance using AWS RAM. The hybrid pattern maintains isolation in development accounts. This extends the previous automatic model registration functionality.

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Original · AWS Machine Learning Blog

Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 2

Governing models across accounts is the next step after automatic model registration. This post extends managed MLflow and Amazon SageMaker AI Model Registry sync to two cross-account governance topologies: a hub-and-spoke pattern that centralizes governance with AWS RAM, and a hybrid pattern that keeps development accounts isolated.

#Amazon SageMaker#MLflow#AWS RAM#model governance#cross-account

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