Automating Azure Machine Learning Jobs with GitHub Actions

Manual machine learning workflows slow down deployment and introduce errors. Transitioning to automated MLOps ensures your models are trained, tested, and updated reliably every time your code changes. This text-based course guides you through the process of setting up automated pipelines. You will read clear explanations, study structured YAML configurations, and learn how to connect your repository to cloud resources to trigger training runs automatically. What you'll learn: - Understand core MLOps principles and the fundamentals of workflow automation - Configure secure connections between your repository and cloud environments using modern credential management - Write GitHub Actions workflows to automate Azure Machine Learning job execution - Manage data assets, environments, and model registration through automated scripts - Monitor job status and handle errors within your continuous integration pipeline Starting with fundamental MLOps terminology and credential setup, the course moves step-by-step through writing workflow files, configuring triggers, and managing outputs. You will work through written examples and practical code snippets to build a functional automation pipeline. This course is designed for data scientists, software developers, and aspiring DevOps engineers who are new to MLOps. No prior experience with automated pipelines is required, though a basic understanding of machine learning concepts is helpful. Start reading today to streamline your machine learning workflows with modern automation practices.