Building and Automating Pipelines in Azure Machine Learning

Scaling machine learning projects requires moving beyond manual experimentation to robust, automated workflows. This text-based course guides you through the process of structuring, running, and scheduling machine learning pipelines using Azure Machine Learning. By reading through clear, step-by-step explanations and studying practical configuration examples, you will transition from running isolated scripts to deploying fully automated, repeatable ML pipelines. You will gain the confidence to structure components, manage data assets, and track your pipeline runs efficiently. What you'll learn: - Understand foundational pipeline concepts and the architecture of Azure Machine Learning. - Create reusable pipeline components to modularize your data preparation and training code. - Build and run end-to-end pipelines using the SDK and YAML configurations. - Schedule pipeline runs to automate model retraining based on time or data changes. - Track and monitor pipeline jobs using integrated MLflow logging. - Apply best practices for managing environment dependencies and compute targets safely. The course starts with essential terminology and design patterns before guiding you through component creation, pipeline assembly, and automated scheduling. You will practice by analyzing structured code templates and configuration files. This course is designed for data scientists, ML engineers, and developers who are new to Azure Machine Learning pipelines. No prior cloud engineering experience is required, though basic familiarity with Python is helpful. Start reading today to streamline and automate your machine learning workflows.