Chapter 7: Model Deployment and Automation

In the previous chapter, you saw how the platform enables you to build and register the model in an autonomous fashion. In this chapter, we will extend the machine learning (ML) engineering domain to model deployment, monitoring, and automation of deployment activities.

You will learn how the platform provides the model packaging and deployment capabilities and how you can automate them. You will take the model from the registry, package it as a container, and deploy the model onto the platform to be consumed as an API. You will then automate all these steps using the workflow engine provided by the platform.

Once your model is deployed, it works well for the data it was trained upon. The real world, however, changes. You will see how the platform allows you to observe your model's performance. This chapter discusses the tools and techniques to monitor your model performance. The performance data could be used to decide whether...