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

- Company: Amazon (amazon.com)
- Announced: 2026-09-08T17:03:50+00:00
- Category: capability-change
- Coverage: not counted
- Announcement: no
- Group: routine
- Source: https://aws.amazon.com/blogs/machine-learning/govern-models-with-mlflow-and-amazon-sagemaker-ai-model-registry-sync-part-2/
- Record: https://forck.live/items/9287-govern-models-with-mlflow-and-amazon-sagemaker-ai-model-registry-sync-part-2
- Subject: Bedrock / Nova

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.

## Evidence

Verbatim from https://aws.amazon.com/blogs/machine-learning/govern-models-with-mlflow-and-amazon-sagemaker-ai-model-registry-sync-part-2/:

> 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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