Running and Tracking Jobs in Azure Machine Learning

Scaling your machine learning models requires a reliable way to run, monitor, and manage your training workloads in the cloud. Azure Machine Learning provides a robust platform for orchestrating these tasks efficiently, but getting started with cloud-based jobs can feel overwhelming. This text-based course guides you from the foundational concepts of cloud compute to executing and tracking your own machine learning runs. You will learn to use the Azure CLI to automate your workloads, manage data inputs and outputs, and establish basic MLOps practices without relying on complex graphical interfaces. What you'll learn: 1. Understand foundational cloud machine learning concepts and key terminology. 2. Configure and submit machine learning jobs using the Azure CLI. 3. Manage job inputs and outputs to handle training data securely in the cloud. 4. Track and monitor job execution and performance metrics through structured logs. 5. Apply basic MLOps principles to make your training runs reproducible. 6. Troubleshoot common execution errors using written log files. You will start by exploring core definitions and setup requirements before moving on to writing configuration files and executing jobs. The course wraps up with practical guidance on monitoring runs and managing data assets for reproducible machine learning workflows. Designed for beginner data scientists, machine learning enthusiasts, and cloud practitioners new to Azure Machine Learning, this course requires no prior cloud experience, though a basic understanding of Python is helpful. Start reading today to take control of your machine learning workloads in the cloud.