This module explores the infrastructure required to build, train, and operationalize machine learning workflows in Azure Machine Learning. Learners will gain hands-on experience setting up compute instances and clusters, constructing visual ML pipelines using Azure ML Designer, integrating custom Python code, and evaluating execution outputs. The module also covers troubleshooting errors and reviewing module results to ensure workflow reliability and model performance.

This module provides learners with the skills to automate and customize machine learning workflows using the Azure Machine Learning SDK. It introduces the setup of the SDK environment, creating and managing workspaces programmatically, executing model training and experimentation workflows, and implementing AutoML and HyperDrive for advanced automation and tuning. Through hands-on code-driven activities, learners gain experience working with scripts, experiments, pipelines, and hyperparameter optimization.

This module focuses on operationalizing machine learning models by guiding learners through model registration, endpoint deployment, and pipeline publishing using Azure Machine Learning. It covers production-ready compute options, real-time and batch inference deployments, and concludes with best practices for wrapping up a complete ML workflow. By the end of this module, learners will be equipped to transition from experimentation to scalable deployment using both the Designer and SDK approaches.