What you'll learn

Machine learning teams stall when their platform is hand-built, undocumented, and impossible to reproduce. In this course, AI-300: Designing and Implementing an MLOps Infrastructure, you'll gain the ability to design and operate Azure Machine Learning infrastructure the way a platform team should. First, you'll explore how to create and manage the core resources of a workspace, including datastores, compute targets, and the identity and access model that governs them. Next, you'll discover how to create, version, and share the assets your team reuses every day — data assets, environments, and components — across workspaces using registries. Finally, you'll learn how to organize the entire environment as infrastructure as code, deploying workspaces with Bicep and the Azure CLI, automating provisioning through GitHub Actions with no stored secrets, and restricting network access to meet security requirements. When you're finished with this course, you'll have the skills and knowledge of Azure Machine Learning infrastructure needed to provision, secure, and reproduce an MLOps platform on demand.