AI-300: Operationalizing Machine Learning and Generative AI Solutions

Learn what it means to operationalize machine learning and generative AI in production. In this orientation you will build the mental model that ties every subsequent lesson together — the shared discipline that turns a trained model or a working prompt into a reliable service. You will leave able to explain how MLOps and GenAIOps share infrastructure yet diverge on lifecycle, and to map every AI-300 exam objective onto a concrete production surface.

Learn how the components of a production machine learning platform fit together. You will build a working mental model of how workspaces, datastores, data assets, environments, and reusable components combine to support the full model lifecycle, and how infrastructure as code delivers that platform reliably from source control. After this lesson you will be able to read a production ML platform's architecture and reason about how new capabilities extend it.

Learn how modern machine learning teams manage experiments and produce trustworthy models. You will explore how experiment tracking, hyperparameter tuning, automated model exploration, and responsible-AI evaluation combine to move a model from an idea to a registered, evaluated artifact ready for deployment. After this lesson you will be able to design an end-to-end training workflow that captures every experiment and produces production-ready models.

Learn how production machine learning deployments work and how to keep them healthy over time. You will explore progressive rollout strategies for new model versions, traffic mirroring for shadow testing, data-drift detection, and automated retraining triggers. After this lesson you will be able to design a safe deployment strategy for a model and specify the monitoring signals that catch performance degradation before customers notice.

Learn how generative AI applications get deployed and operated in production. You will understand the Microsoft Foundry hierarchy of accounts, projects, and model deployments, how to choose deployment types for different workloads, and how to version prompts alongside application code. After this lesson you will be able to design the platform architecture for a Foundry-hosted generative AI application.

Learn how to evaluate the quality and safety of generative AI applications and observe them in production. You will explore the built-in evaluator taxonomy, how to build a golden test dataset, how to configure a safety battery for both pre-merge CI and continuous monitoring, and how to unify traces, metrics, evaluations, and cost into a single view. After this lesson you will be able to design a complete evaluation and observability strategy for any generative AI application.

Learn how to systematically improve the accuracy and efficiency of generative AI systems. You will explore the levers that tune retrieval-augmented generation quality, how hybrid search improves recall on structured queries, and when and how fine-tuning is worth the investment. After this lesson you will be able to plan a quantitative optimization campaign for a generative AI application with clear success criteria.