Azure Machine Learning Workbench is a front-end for a variety of tools and services, including the Azure Machine Learning Experimentation and Model Management services.
Workbench is a relatively open toolkit. First, you can use almost any Python-based machine learning framework, such as Tensorflow or scikit-learn. Second, you can train and deploy your models either on-premises or on Azure.
Workbench also includes a great data-preparation module. It has a drag-and-drop interface that makes it easy to use, but its features are surprisingly sophisticated.
In this course, you will learn how Workbench interacts with the Experimentation and Model Management services, and then you will follow hands-on examples of preparing data, training a model, and deploying a trained model as a predictive web service.