Monitoring Generative AI Applications with Azure AI Foundry

Deploying a generative AI application is only the first step; keeping it cost-effective, fast, and accurate requires continuous oversight. Understanding how to track and evaluate model behavior in production is essential for building trust and managing operational costs. This text-only course guides you through the foundational concepts of LLM observability and generative AI monitoring. You will learn how to set up tracking systems, interpret performance metrics, and implement evaluation strategies to ensure your applications remain robust, safe, and efficient. What you'll learn: Understand the core principles of LLM observability and performance tracking; Track key operational metrics including token consumption, latency, and API costs; Evaluate model response quality using modern metrics like groundedness and relevance in RAG workflows; Configure Azure AI Foundry to monitor application health and detect anomalies; Analyze cost-performance trade-offs to optimize deployment decisions; Implement logging strategies to safely capture user interactions and model outputs. The course starts with foundational definitions of AI monitoring before walking through step-by-step configuration guides, metric dashboards, and practical optimization scenarios. You will practice through written exercises and conceptual walkthroughs designed to build real-world monitoring skills. This course is designed for beginner AI developers, product managers, and cloud enthusiasts looking to manage generative AI deployments. No prior monitoring experience is required. Start reading today to master the essentials of generative AI observability and keep your applications running smoothly.