This comprehensive Azure Machine Learning course is designed for data scientists, machine learning engineers, and developers who want to build, train, and deploy machine learning models using Microsoft Azure. The course provides a complete overview of Azure ML, starting from fundamental concepts and progressing to advanced MLOps workflows.
Students will begin by understanding the core principles of Azure Machine Learning and how it fits into modern data science pipelines. The course then explores automated machine learning (AutoML) and the Azure ML Designer, enabling users to build and train models without extensive coding.
Learners will also dive into code-first machine learning development, where they build and train models using Python and Azure ML SDK. The course includes model deployment techniques, allowing learners to operationalize their models and make them available for real-world applications.
A major focus of the course is MLOps, including MLflow integration, experiment tracking, and lifecycle management of machine learning models. Students will also learn how to use GitHub Actions with Azure ML to automate pipelines and improve collaboration between development and operations teams.
By the end of the course, learners will be able to design, train, deploy, and manage machine learning models in Azure while implementing best practices for scalable and production-ready MLOps workflows.
