Configuring Azure Machine Learning Pipelines with YAML

To scale machine learning workflows, automation and clear configuration are essential. This text-based course teaches you how to use YAML to define, structure, and control your Azure Machine Learning pipelines. You will progress from understanding basic YAML syntax to writing clean, modular pipeline configurations. By the end of this course, you will be able to automate complex machine learning steps, define reusable components, and manage environments efficiently using industry-standard configurations. What you'll learn: Understand core YAML syntax, indentation rules, and data structures used in cloud configurations; Define Azure Machine Learning pipeline jobs, inputs, and outputs using declarative YAML; Create reusable pipeline components to modularize your training and data preprocessing steps; Configure compute targets, environments, and secrets securely within your pipeline definitions; Apply modern Azure ML CLI practices to submit and monitor automated workflows. The course begins with foundational YAML concepts and structure before guiding you through practical Azure Machine Learning pipeline schemas, components, and automation strategies. Designed for beginners in cloud machine learning and DevOps, this course requires no prior YAML experience, though a basic familiarity with machine learning concepts is helpful. Start reading today to streamline and automate your machine learning workflows with confidence.