Machine Learning Security with Azure

8

Managing and Securing the MLOps Life Cycle

As important as data and infrastructure are, their management can create overhead and take away from the actual ML tasks. When different roles collaborate on an ML project, there is a need to automate and standardize things to make the daily tasks more efficient.

In this chapter, we will explore MLOps best practices and how we can implement them using Azure and other tools. We will dive into how to leverage Infrastructure as Code (IaC) and some applications of DevOps in the ML life cycle for continuous integration/continuous delivery (CI/CD) using Azure DevOps. These are not the only ways to implement MLOps, though. Azure provides us with comprehensive monitoring and logging capabilities, which we ...