Model Training and Management with Azure Machine Learning

Transitioning from local notebooks to cloud-scale machine learning can feel overwhelming without a clear path. Azure Machine Learning provides the robust infrastructure needed to train, track, and manage models reliably at scale. This written course guides you through the entire machine learning lifecycle on the Azure platform. You will gain the practical skills to prepare data, set up secure cloud compute, monitor experiments, and manage your models for production readiness. What you'll learn: • Understand foundational cloud machine learning concepts and Azure workspace architecture • Configure scalable compute resources and secure data assets for training runs • Track experiments and log performance metrics automatically using MLflow integration • Register and version trained models in a centralized registry for better governance • Deploy models to real-time endpoints for generating predictions • Implement modern MLOps best practices to automate and monitor workflows. Starting with essential cloud concepts and workspace setup, the text-based lessons guide you step-by-step through data preparation, model training, and deployment strategies. You will study practical configuration examples and structured explanations designed for immediate application. This course is designed for aspiring data scientists, developers, and cloud enthusiasts who are new to Azure Machine Learning. No prior cloud engineering experience is required, though a basic understanding of Python and machine learning concepts is helpful. Start building and managing your cloud-scale machine learning workflows today.