This Azure MLOps and Machine Learning DevOps course is designed for engineers and data scientists who want to move machine learning models from development to production using Microsoft Azure. The course focuses on building scalable, automated, and production-ready ML workflows using Azure Machine Learning Service, Azure DevOps, and Databricks.

Students will begin by learning how to train machine learning models using Azure Machine Learning Service and how to structure ML projects for production environments. The course then introduces core DevOps concepts applied to machine learning, including CI/CD pipelines and model versioning.

A key focus of the course is deploying machine learning models to different Azure environments, including Azure Container Instances (ACI) for lightweight deployment and Azure Kubernetes Service (AKS) for scalable production workloads. Learners will also understand how to manage deployment strategies based on real-world requirements.

The course also covers Databricks MLOps and MLflow integration, allowing learners to track experiments, manage model versions, and improve reproducibility. Students will learn how to use MLflow Tracking to monitor model performance and streamline the machine learning lifecycle.

By the end of the course, learners will be able to design and implement complete MLOps pipelines, automate model deployment, and manage machine learning systems efficiently using Azure DevOps, Azure ML, and Databricks.