AI-300 Beginner Guide for ML and Generative AI Operations

AI-300 focuses on operating machine learning and generative AI solutions after the prototype stage. The exam expects familiarity with Azure Machine Learning workspaces, compute, environments, model lifecycle tasks, Managed Online Endpoints, Azure AI Foundry, Azure OpenAI, evaluation, monitoring, and optimization.

The exam is best suited for data scientists, machine learning engineers, AI engineers, and platform engineers who help move models or generative AI apps into production. It assumes more than conceptual AI knowledge; candidates should understand Python, Azure resources, command-line tools, and how production systems are deployed and maintained.

Microsoft lists five skill areas: design and implement an MLOps infrastructure, implement machine learning model lifecycle and operations, design and implement a GenAIOps infrastructure, implement generative AI quality assurance and observability, and optimize generative AI systems and model performance. Those names are the safest study outline.

Start with Azure Machine Learning resource basics and Managed Online Endpoints, then connect those ideas to model training, deployment, monitoring, and rollback. For generative AI operations, learn how Azure AI Foundry projects, Model Catalog, Prompt Flow, evaluation, content safety, and token or cost monitoring fit into an operational process.