This end-to-end Azure Machine Learning course provides a complete overview of how to build, train, deploy, and manage machine learning solutions on Microsoft Azure. Designed for learners who want both conceptual understanding and hands-on exposure, the course explores Azure ML from multiple perspectives, including no-code, low-code, and code-first approaches.

The course begins with a high-level overview of machine learning in Azure, explaining core services and how Azure ML fits into real-world data science workflows. Learners then explore Automated Machine Learning and the Azure ML Designer to train and deploy models visually, making it ideal for rapid experimentation and prototyping.

As the course progresses, it introduces code-first programming using Azure ML SDK, including training, scoring, and managing models programmatically. MLflow integration is also covered to help track experiments and manage model versions effectively.

A strong focus is placed on MLOps, including CI/CD concepts, GitHub Actions integration, pipeline automation, and data versioning using DVC. The course also demonstrates how to integrate Azure ML with Power BI, perform batch processing, and troubleshoot deployments locally.

This course is well-suited for data scientists, ML engineers, and Azure professionals seeking practical, production-ready machine learning skills.