AWS details cross-account MLflow and SageMaker model governance topologies
AWS has published a technical guide outlining two cross-account architectures for governing machine learning models using MLflow and Amazon SageMaker AI. The post extends previous single-account workflows to address the needs of larger organizations that separate development and production environments at the account level. The first proposed topology is a hub-and-spoke model, where a central…
Key points
- AWS introduces hub-and-spoke and hybrid topologies for cross-account ML model governance using MLflow and SageMaker.
- The hybrid topology ensures development accounts remain isolated, copying models to the hub only after local approval.
- AWS RAM and IAM condition keys are used to enforce strict separation between data scientists and governance officers.
The second topology is a hybrid approach designed for regulated industries requiring strict isolation. In this setup, development accounts maintain their own local registries. Models are only copied to the central hub after local approval by a model owner, ensuring that development workloads never write directly into the production-grade governance account. The guide details the necessary IAM policies, S3 bucket permissions, and EventBridge rules required to automate these workflows. It also covers how approved models trigger CI/CD pipelines for deployment to SageMaker endpoints, providing a comprehensive framework for secure, multi-account MLOps.
The story so far
10 episodes →- AWS details cross-account MLflow and SageMaker model governance topologiesthis story
Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 2
AWS Machine Learning Blog · 8 September 2026
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This text was published by AWS Machine Learning Blog and written by Melanie Li. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗
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