Batch ML Deployment with Azure CLI

Running machine learning models on large datasets requires efficient, automated, and scalable infrastructure. Deploying batch endpoints using the command-line interface simplifies this process, allowing you to run predictions over massive datasets without manual intervention. In this course, you will transition from manual model evaluation to automated batch scoring. You will learn how to package your machine learning models, set up batch endpoints, and manage deployments using the Azure CLI. Through clear text explanations and structured configuration examples, you will build a robust understanding of production-ready batch inference workflows. What you'll learn: - Understand the core concepts of batch endpoints, deployments, and compute clusters in Azure Machine Learning. - Configure YAML files to define your models, environments, and deployment settings. - Deploy both MLflow and custom non-MLflow models for high-throughput batch scoring. - Manage and scale batch jobs using simple, repeatable command-line instructions. - Integrate model deployment into basic MLOps automation workflows. - Monitor job execution and troubleshoot common deployment errors through log analysis. The course begins with foundational definitions of batch processing and command-line infrastructure, guiding you step-by-step through configuration files, deployment commands, and production management practices. You will practice through written scenarios and configuration exercises designed to solidify your command-line skills. This course is designed for beginner data scientists, machine learning enthusiasts, and cloud practitioners who want to automate model scoring. No prior cloud deployment experience is required, though a basic familiarity with machine learning concepts is helpful. Start reading today to master automated batch scoring with the command line.