Selecting, Deploying, and Evaluating Azure AI Foundry Models

Selecting the right foundation model for your software application can be overwhelming with hundreds of options available. This text-based course guides you through the complete lifecycle of choosing, launching, and assessing models using the AI Foundry portal. By reading our structured explanations and reviewing practical configuration examples, you will gain the confidence to integrate top-tier generative AI models into your workflows. You will transition from guessing which model works best to making data-driven decisions based on clear performance metrics. What you'll learn: Select the ideal foundation model by comparing benchmarks and capabilities in the model catalog; Deploy models to real-time endpoints for seamless application integration; Evaluate model performance using both manual test prompts and automated evaluation pipelines; Apply key evaluation metrics such as accuracy, latency, and coherence to measure model quality; Understand modern LLM evaluation concepts, including LLM-as-a-judge and responsible AI safety guardrails. The course begins with foundational concepts of model hubs and benchmarking, then walks you through step-by-step deployment strategies, and concludes with advanced manual and automated evaluation techniques. This course is designed for beginner AI engineers, developers, and product managers who want to understand the model lifecycle. No prior machine learning experience is required. Start reading today to master the essentials of AI model management.