Azure Machine Learning Model Monitoring Fundamentals

Deploying a machine learning model is only the beginning of its lifecycle. Once in production, models can degrade, encounter unexpected data, and drift over time, making continuous tracking essential. This text-based course guides you through the core concepts of monitoring machine learning models within the Azure ecosystem. You will learn how to set up data collection, analyze model performance, and establish alerts to keep your intelligent systems running smoothly. What you will learn: - Understand foundational MLOps and model monitoring terminology; - Configure Azure Machine Learning data collectors to gather production inputs and outputs; - Detect data drift and model performance degradation over time; - Set up alerts and automated triggers to respond to operational anomalies; - Practice tracking modern generative AI and LLM metrics alongside classic ML metrics; - Integrate monitoring workflows with Azure Monitor. We begin with the core definitions of model drift and operational metrics, then transition into configuring Azure-native monitoring tools and analyzing real-world telemetry data. This course is designed for beginners, aspiring data scientists, and cloud enthusiasts who want to understand model observability. No prior experience with Azure Machine Learning is required. Start reading today to master the essentials of keeping your deployed models accurate and reliable.