Introduction to MLOps: Build and Deploy Pipelines with Azure

Many machine learning models never make it past the experimental phase because transitioning them to production requires a systematic, automated approach. This course introduces you to MLOps, the essential discipline that unites machine learning development with operations to deliver reliable, scalable models in the real world. Through this comprehensive written guide, you will transition from manual model training to designing automated, production-ready workflows. You will understand how to apply DevOps principles to data science, enabling automated testing, continuous integration, and systematic model deployment. What you'll learn: - Understand the core concepts of MLOps, including maturity levels, life cycle phases, and the challenges of traditional machine learning deployment. - Design Continuous Integration (CI) and Continuous Delivery (CD) pipelines tailored specifically for machine learning workflows. - Configure Continuous Training (CT) loops to automatically retrain models when new data arrives. - Apply Azure Machine Learning and Azure DevOps concepts to orchestrate end-to-end machine learning pipelines. - Monitor deployed models in production using drift detection and performance tracking strategies to ensure long-term reliability. You will begin by learning fundamental MLOps terminology and architectural principles before exploring step-by-step written walkthroughs of pipeline configurations. The material covers everything from versioning data and models to automating deployments and setting up production monitoring. This course is designed for aspiring ML engineers, data scientists, and developers who are new to operations and want to build a solid foundation. No prior DevOps experience is required, as we start with the absolute basics of pipelines and automation. Start reading today to master the workflows that bring machine learning models to life in production.