This course is designed to provide a comprehensive foundation in Azure Machine Learning, equipping learners with essential skills for managing ML workflows within the Azure ML workspace. Participants will begin by understanding core workspace fundamentals, including environment setup, resource management, and key components for ML experimentation. The course progresses to advanced concepts such as optimizing compute resources, managing datasets effectively, and configuring high-performance ML pipelines.

Learners will gain expertise in scaling ML workloads, fine-tuning data storage strategies, and applying best practices for secure and efficient model deployment. Additionally, the course covers advanced data and compute management techniques to enhance ML operations (MLOps) and ensure seamless integration with Azure services. This course is structured into multiple modules, each featuring lessons and video lectures that provide theoretical insights and hands-on practice. Participants will engage with approximately 3:00–4:00 hours of instructional content, ensuring both conceptual understanding and practical application. To reinforce learning, graded and ungraded assignments are included within each module to test the ability of learners in real-world scenarios. Module 1: Experiment with Azure Machine Learning Module 2: Deploying, Consuming, Managing, and Evaluating Models with Azure Machine Learning By the end of this course, a learner will be able to Explore the process of registering, logging, and deploying MLflow models Understand and implement Responsible AI practices Understand the fundamentals of AutoML in Azure Learn about different machine learning algorithms and tasks Master how to interpret AutoML job results, ensuring success and optimizing model performance.