Summary
In this chapter, you learned about the problems MLOps aims to tackle and how it can increase the velocity of your data science initiatives. You also refreshed your knowledge of Kubernetes and OpenShift and saw how Red Hat OpenShift provides a consistent and reliable environment where you can run your container workloads on-premises and in the cloud. You have seen how RHODS, using the strengths of the underlying container platform, provides a full set of components for an MLOps platform.
In the next chapter, you will learn about the stages of the ML life cycle, as well as the role MLOps plays in implementing all the stages of model development and deployment. You will also see how teams collaborate during model development and deployment stages and how RHODS components relate to each stage of the ML life cycle.
