MLOps on Azure: Deploy and Manage Machine Learning Models

Moving machine learning models from a local notebook to a reliable production environment is one of the biggest challenges in AI engineering today. This text-based course guides you through the foundational principles of Machine Learning Operations (MLOps) using Azure. You will transition from writing isolated data science code to building automated, reproducible, and monitored machine learning pipelines. By understanding the core workflows of Azure Machine Learning, you will gain the skills to operationalize models confidently and maintain them over time. What you'll learn: - Understand the core concepts of MLOps, including model reproducibility, versioning, and the machine learning lifecycle. - Configure Azure Machine Learning workspaces and assets to manage data, environments, and compute resources. - Deploy trained models as web services and real-time endpoints for production use. - Implement automated pipelines to streamline model training, evaluation, and registration. - Monitor model performance and data drift in production to ensure long-term reliability. - Apply modern CI/CD practices to automate your machine learning workflows. The course begins with essential MLOps terminology and foundational cloud concepts before guiding you through practical pipeline configurations and deployment strategies. You will read detailed explanations and study real-world configuration examples designed to build your engineering skills. This course is designed for aspiring AI engineers, data scientists, and developers who are new to MLOps. No prior cloud engineering or advanced DevOps experience is required. Start reading today to master the essentials of modern model deployment on Azure.