MLflow in Azure Machine Learning: Run Scripts and Track Models

Transitioning from local machine learning experiments to scalable cloud training can feel overwhelming without the right tracking tools. Managing metrics, parameters, and model artifacts in a collaborative environment requires a structured approach to experiment logging. This text-based course guides you through the foundational concepts of MLflow and Azure Machine Learning, showing you how to seamlessly run training scripts in the cloud and track your model's performance over time. What you will learn: Understand foundational MLOps concepts, workspace structures, and MLflow integration; Configure and submit Python training scripts as command jobs in Azure Machine Learning; Track training parameters, custom metrics, and output logs systematically; Apply MLflow autologging to automatically capture model metrics with minimal code; Register and manage model versions in the centralized cloud registry; Analyze run histories and compare performance across different training iterations. You will begin by exploring core definitions and setup procedures, then progress to writing tracking code and managing runs in the cloud. Each concept is reinforced with clear written explanations and structured code snippets. This course is designed for data scientists, machine learning enthusiasts, and developers who are new to cloud-based MLOps, with no prior experience in Azure Machine Learning or MLflow required. Start organizing your machine learning experiments in the cloud today.