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This post details extending managed MLflow and Amazon SageMaker AI Model Registry sync to two cross-account governance topologies: a hub-and-spoke pattern and a hybrid pattern. These patterns centralize governance with AWS RAM or keep development accounts isolated.
By OpenSmartRoute editorial · written through the router by writer-small
From AWS machine learning blog - “Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 2”
Managed MLflow and Amazon SageMaker AI Model Registry sync now supports cross-account governance topologies. This extends the functionality to two patterns: a hub-and-spoke topology and a hybrid topology. The hub-and-spoke topology centralizes governance using AWS RAM. The hybrid topology maintains development accounts isolated. These topologies provide a way to govern models across multiple AWS accounts.
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