MLflow Model Management in Azure Machine Learning

Bridging the gap between training machine learning models and managing them in production can be challenging without a structured registry. This text-based course guides you through the process of using MLflow inside Azure Machine Learning to log, register, and manage your model lifecycles efficiently. You will gain hands-on understanding of how to transition models from local training runs to cloud-managed assets. What you will learn: - Understand foundational model registry concepts and the role of MLflow in modern MLOps. - Configure Azure Machine Learning workspaces to track MLflow experiments. - Register trained MLflow models with version control and custom metadata. - Manage model environments and dependencies using reproducible packaging standards. - Prepare registered models for deployment to cloud endpoints. You will start with core definitions of machine learning lifecycles and tracking terminology before moving into step-by-step written explanations, code snippets, and configuration examples for tracking and deployment. This course is designed for beginner data scientists and machine learning engineers looking to organize their model workflows, requiring only basic Python knowledge. Start organizing your machine learning models today.