Azure Machine Learning Pipelines: Building End-to-End Workflows

Scaling machine learning models from local experiments to production requires robust, automated workflows. This text-based course guides you through the process of structuring, executing, and managing end-to-end machine learning pipelines on the Azure platform. You will learn how to transition from manual scripts to automated, repeatable pipelines. By studying our step-by-step written guides and analyzing real-world configuration examples, you will gain the skills to build sequential and parallel jobs, manage datasets, and deploy models efficiently. What you'll learn: - Understand foundational cloud MLOps concepts and Azure Machine Learning workspace architecture. - Configure environment dependencies and compute targets using the Azure CLI. - Build sequential pipeline steps to automate data preparation, training, and evaluation. - Design parallel jobs to process large-scale datasets and optimize training time. - Register and track models using modern Azure ML registry and versioning practices. - Monitor pipeline runs and troubleshoot common execution errors through log analysis. The course starts with essential terminology and workspace setup before guiding you through YAML-based pipeline definitions and CLI commands. You will progress from simple two-step workflows to complex, parallelized production-ready pipelines. This course is designed for aspiring ML engineers, data scientists, and cloud enthusiasts who are new to Azure Machine Learning. No prior cloud engineering experience is required, though a basic understanding of Python is helpful. Start building structured, scalable cloud workflows today.