Build and Operate Machine Learning Solutions with Azure

Module 1 introduces participants to using the Azure Machine Learning SDK to train models, covering topics such as creating workspaces, running experiments, and registering models. Participants will also learn how to manage Azure resources for machine learning and run training scripts.

Module 2 focuses on working with data and compute in Azure Machine Learning. Participants will learn about datastores, environments, compute targets, and datasets. The module also covers creating and using environments and compute targets.

Module 3 delves into orchestrating pipelines and deploying real-time machine learning services with Azure Machine Learning. Topics include troubleshooting service deployment, reusing pipeline steps, publishing pipelines, and deploying and consuming real-time inferencing services.

Module 4 covers deploying batch inference pipelines and tuning hyperparameters with Azure Machine Learning. Participants will learn to create and publish batch inference pipelines, configure sampling, and run hyperparameter tuning experiments.

Module 5 focuses on selecting models and protecting sensitive data. Participants will explore automated machine learning tasks and algorithms, understand differential privacy, configure data privacy parameters, and create explanations for interpreting models.

Module 6 revolves around monitoring machine learning deployments. Participants will consider model fairness, analyze and mitigate unfairness using Fairlearn, enable Application Insights, and create data drift monitors.