AWS Multi-Account MLOps: Hub-and-Spoke Deployment Strategies

Deploying machine learning models in a single environment is straightforward, but enterprise-grade MLOps requires isolating development, testing, and production workflows across multiple cloud accounts. This text-based course guides you through the industry-standard hub-and-spoke architecture to manage and deploy models securely at scale. You will transition from basic single-account setups to designing secure, multi-account MLOps pipelines. By understanding how to centralize model governance while decentralizing deployments, you will gain the skills needed to implement robust, enterprise-ready production workflows. What you will learn: Understand the core architectural patterns of hub-and-spoke multi-account environments; Configure SageMaker Model Registry to act as a centralized hub for model governance; Apply AWS Resource Access Manager (RAM) to securely share resources across account boundaries; Implement modern MLOps security principles, including least-privilege IAM policies and zero-trust foundations; Design automated deployment workflows that safely promote models from staging to production accounts; Practice fundamental infrastructure-as-code concepts to keep multi-account configurations consistent and reproducible. The course begins with foundational multi-account concepts and key MLOps terminology before moving step-by-step through registry setup, secure resource sharing mechanics, and deployment pipeline design. You will read detailed architectural explanations and conceptual configuration snippets to reinforce your learning. This course is designed for beginner-level cloud engineers, data scientists, and aspiring MLOps professionals who want to learn enterprise deployment strategies; no prior multi-account experience is required. Start building secure, scalable machine learning pipelines today.