Azure Machine Learning: Build and Deploy Your First Project

Transitioning from local machine learning experiments to scalable cloud deployments can feel overwhelming. This text-based course guides you step-by-step through the process of building, managing, and publishing machine learning workflows in the cloud. You will transition from writing basic local scripts to managing full-lifecycle machine learning projects on Azure. By exploring core concepts and practicing with written walkthroughs, you will gain the confidence to configure cloud environments, register datasets, train models, and expose them as production-ready endpoints. What you'll learn: Understand foundational cloud machine learning concepts and terminology; Configure Azure Machine Learning workspaces, compute instances, and environments; Manage data assets and register datasets for reproducible training runs; Train and track machine learning models using modern MLflow integration; Deploy trained models as real-time inference endpoints for applications; Apply basic MLOps practices to monitor and update your cloud deployments. The course begins with essential terminology and workspace setup before moving into data preparation, model training, and deployment strategies. You will read detailed explanations and study structured code snippets designed to make cloud architecture easy to understand. This course is designed for aspiring data scientists, developers, and beginners who want to learn cloud-based machine learning. No prior experience with cloud platforms is required, though a basic understanding of Python is helpful. Start reading today to build and publish your first cloud-based machine learning project.