Data is the foundation of machine learning. In this module, you will learn how to work with datastores and datasets in Azure Machine Learning, enabling you to build scalable, cloud-based model training solutions. You'll also learn how to use cloud compute in Azure Machine Learning to run training experiments at scale.

Orchestrating machine learning training with pipelines is a key element of DevOps for machine learning. In this module, you'll learn how to create, publish, and run pipelines to train models in Azure Machine Learning. You'll also learn how to register and deploy ML models with the Azure Machine Learning service.

Machine learning models are often used to generate predictions from large numbers of observations in a batch process. You will accomplish this using Azure Machine Learning to publish a batch inference pipeline. You will also leverage cloud-scale experiments to choose optimal hyperparameter values for model training.

In this module, you will learn how to use automated machine learning in Azure Machine Learning to find the best model for your data. You will learn how differential privacy is a leading edge approach that enables useful analysis while protecting individually identifiable data values. You will also learn about the factors that influence the predictions models make.

Machine learning models can often encapsulate unintentional bias that results in unfairness. In this module, you will learn how to use Fairlearn and Azure Machine Learning to detect and mitigate unfairness in your models. You will learn how to use telemetry to understand how a machine learning model is being used once it has been deployed into production. Finally, you will learn how to monitor data drift to ensure your model continues to predict accurately.