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Amazon· Bedrock / NovaGovern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 2
Amazon describes how to extend MLflow and SageMaker AI Model Registry sync to cross-account governance, presenting a hub-and-spoke topology for centralized governance and a hybrid topology for regulated environments, and shows how an approved model moves from the registry to a deployed endpoint through CI/CD.
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In this post, we extend the same building blocks to two cross-account governance topologies. The first is a hub-and-spoke pattern that centralizes governance by sharing one MLflow app across development accounts with AWS Resource Access Manager (AWS RAM). The second is a hybrid pattern for regulated environments that keeps development accounts fully isolated from the governance hub. We close by showing how an approved model moves from the registry to a deployed endpoint through continuous integration and continuous delivery (CI/CD), and we compare the topologies to help you choose one.
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