This Azure MLOps course provides a comprehensive guide to applying DevOps practices to machine learning projects. Participants begin with an introduction to Azure Machine Learning Service and Azure DevOps, understanding the tools needed for end-to-end MLOps pipelines. The course guides learners through setting up Azure DevOps configurations and creating infrastructure as code pipelines, ensuring scalable and repeatable deployments. Detailed lessons cover Continuous Integration (CI) pipelines for model training, automated training workflows, and Continuous Deployment (CD) pipelines for staging and production environments. Participants learn to test, monitor, and validate their MLOps pipelines, ensuring models perform reliably in production. Practical examples demonstrate deploying ML models in real-world scenarios, integrating best practices for automation, reproducibility, and collaboration. By the end of the course, learners will be able to implement robust MLOps pipelines on Azure, automate model workflows, and deploy models confidently from development to production. This course is ideal for data scientists, ML engineers, and DevOps professionals looking to operationalize machine learning efficiently using Azure tools.