Overview

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.

Syllabus

Experiment with Azure Machine Learning

This course provides a deep dive into identifying appropriate data sources, formats, and ingestion strategies for machine learning projects in Azure, ensuring efficient data handling. It emphasizes the principles of selecting the right services and compute options for model training, optimizing performance and scalability. Participants will gain expertise in differentiating between real-time and batch deployment strategies based on consumption needs, enabling informed architectural decisions. Additionally, the course explores MLOps best practices, guiding learners through the design and implementation of scalable workflows and effective Azure ML environment organization, ensuring seamless integration and lifecycle management.

Deploying, Consuming, Managing, and Evaluating Models with Azure Machine Learning

This module provides a comprehensive understanding of deploying, registering, and managing machine learning models within Azure Machine Learning, equipping learners with the skills to operationalize ML solutions. Participants will explore concepts such as deploying models to managed online endpoints, MLflow model registration, and applying Blue-Green deployment strategies for seamless updates. The module covers logging and autologging ML models using MLflow, configuring model signatures, and understanding the MLflow model format to enhance interoperability. Learners will gain expertise in Responsible AI practices, including evaluating the Responsible AI dashboard, performing error analysis, and exploring explanations, counterfactuals, and causal analysis. Additionally, the module includes exam tips to help learners succeed in Azure ML certification. By the end of this module, participants will be equipped with practical knowledge to deploy and manage ML models efficiently while ensuring ethical and responsible AI implementation in Azure Machine Learning.