Summary

In this chapter, we saw a brief explanation of MLOps and how valuable it is in ML projects. By using MLOps tools and best practices, we can streamline our ML tasks to facilitate efficiency and collaboration.

Although MLOps has tools and practices that range from data, models, deployments, and development, we focused more on how we can use IaC to handle our resources, and how to implement CI/CD using DevOps. Although using established code development tools offers the most common ways of working, when it comes to Azure, they are not the only ones. As Azure collects several logs and events in its services, we can leverage those to automate and create our own custom workflows using other Azure services and tools. The logs that Azure collects about its services can be used for more than telemetry and reporting.

Let us move on to the next chapter now, in which we will see how we can use the Azure Monitor service for logging, monitoring, and threat detection.