Designing Machine Learning Solutions with Azure

Transitioning from writing local machine learning code to designing enterprise-grade solutions requires a structured approach to cloud resources, data pipelines, and model deployment. This text-based course guides you through the fundamental principles of architecting scalable machine learning systems using Azure Machine Learning. You will learn how to design, train, and manage the lifecycle of machine learning models with cloud-based workflows. What you'll learn: Understand core cloud machine learning concepts and Azure Machine Learning architecture; Configure secure data storage, compute targets, and workspaces for collaborative development; Design automated training pipelines and manage experiments efficiently; Deploy trained models to production environments for real-time and batch scoring; Apply modern MLOps principles, including basic model monitoring and version control; Manage model artifacts, environments, and register models for reproducible workflows. Starting with foundational definitions of cloud-based machine learning, you will progress through workspace setup, data management, pipeline design, and deployment strategies. This course is designed for aspiring data scientists, developers, and cloud architects who are new to Azure Machine Learning, with no prior cloud experience required. Start building reliable cloud ML solutions today.