MLOps and GenAIOps on Azure — Take Models from Notebook to Production

The AI-300 certifies the Machine Learning Operations Engineer — the role that owns everything that happens after a model works on someone's laptop. This is deliberately not a data science course. Every topic is approached through an operations lens: how do you automate it, version it, monitor it, secure it, and roll it back when it degrades. Training orchestration here means pipeline design, not model architecture.

The course covers both halves of the modern MLOps mandate. The first is classical machine learning operations on Azure Machine Learning — workspaces and compute, assets and environments defined as infrastructure as code, training pipeline orchestration, model registration and feature retrieval, deployment to managed endpoints, and monitoring with automated retraining. The second is GenAIOps, the operational discipline around generative AI: deploying and managing foundation models, running the Foundry Agent Service, versioning prompts as first-class artifacts, optimizing RAG systems, fine-tuning, and the evaluation and observability tooling that tells you whether any of it is actually working.

That evaluation and observability material is where the course spends real time, because it is where generative AI projects most often fail in production. You will work through systematic evaluation and validation of generative outputs, then instrument live solutions so regressions surface before your users find them — alongside the security, identity, and responsible AI practices that keep the whole pipeline governed.