MLOps on Azure: Automating ML Workflows with Data Factory

Moving machine learning models from a local notebook to a reliable production environment can be challenging. This text-based course guides you through automating that transition using Azure MLOps and Data Factory. You will learn how to build automated pipelines that orchestrate data movement, trigger model training, manage versioning, and monitor deployments in production. Learn core MLOps principles, key terminology, and foundational workflow concepts. Configure Data Factory to orchestrate and automate data preparation pipelines. Register and version machine learning models using a centralized model registry. Build automated pipelines to deploy models safely and consistently. Monitor model performance in production and understand basic drift detection concepts. Apply security and access control best practices within your cloud environment. Starting with fundamental cloud and MLOps concepts, the course guides you step-by-step through setting up data pipelines, deploying models, and establishing continuous monitoring. This course is designed for data scientists, developers, and aspiring engineers who want to learn automation basics. No advanced DevOps experience is required. Start reading today to master the foundations of automated machine learning operations.