Train and Track Machine Learning Models with MLflow in Fabric

Managing machine learning experiments can quickly become chaotic without a systematic way to track parameters, metrics, and models. This course teaches you how to bring order to your data science workflow by leveraging MLflow inside the Fabric environment. You will transition from manual tracking to an automated, reproducible workflow, learning how to log runs, compare model performance, and register your best models. What you'll learn: 1. Understand foundational machine learning tracking concepts and MLflow architecture. 2. Configure Fabric notebooks to log parameters, metrics, and artifacts. 3. Train machine learning models and track their performance. 4. Compare multiple experiment runs to identify the best-performing configurations. 5. Register and version your trained models within the Fabric workspace. 6. Apply clean coding practices to ensure reproducible experiments. The course begins with essential terminology and setup, guiding you through basic tracking concepts before moving into notebook examples and model registration. You will learn through written explanations, clear code snippets, and practical scenarios. This course is designed for beginners, aspiring data scientists, and developers who are new to MLflow or Fabric, with no prior experiment tracking experience required. Start organizing your machine learning experiments and build a solid foundation in MLOps today.