3. Data experimentation and visualization using Azure

In the previous chapter, we learned how to navigate different Azure services for implementing ML solutions in the cloud. We realized that the best service for training custom ML models programmatically and automating infrastructure and deployments is Azure Machine Learning. In this chapter, we will set up the Azure Machine Learning workspace, create a training cluster, and perform data experimentation while collecting all artifacts in Azure.

First, you will learn how to prepare and interact with your ML workspace. Once set up, you will be able to perform and track experiments in Azure, as well as trained models, plots, metrics, and snapshots of your code. This can all be done from your authoring Python environment; for example, Jupyter using Azure Machine Learning compute instances—similar to Data Science VMs (DSVMs) or any Python interpreter running in PyCharm, VS Code, and so on. We will first run experimentation...