Summary

In this chapter, we covered the basics of the ML life cycle and how it applies to Azure Machine Learning components. This knowledge is essential not only for data scientists and developers, but also for IT administrators and security engineers who are required to know the basics of ML development to ensure they can secure and monitor all associated services. For anyone wanting to get more familiar with Azure Machine Learning, you can always come back and recreate the scenario presented at the beginning as a base to follow along with the implementations and methods presented in the rest of the book’s chapters.

Together, we learned what the Zero Trust strategy is and how it can be applied to Azure Machine Learning components and their associated services to assess what needs to be secured. We will need Zero Trust, as the principles and the defense areas outlined in this strategy are the same ones we will use in our security implementations in the following chapters...