AWS has published Part 2 of a series on governing machine learning models with managed MLflow and the Amazon SageMaker AI Model Registry sync. The post moves beyond automatic model registration to address governance across multiple accounts. It describes two cross-account topologies: a hub-and-spoke pattern that centralizes governance using AWS Resource Access Manager (RAM), and a hybrid pattern that keeps development accounts isolated.
Why it matters
Organizations that operate ML workloads across multiple accounts need consistent ways to register, track, and govern models. The two documented patterns offer different trade-offs between centralized control and account isolation.
Who should care
Teams using Amazon SageMaker AI and MLflow for model management, as well as those responsible for ML governance in multi-account AWS environments.