Summary
In this chapter, we utilized the model registry available in SageMaker to register, organize, and manage our ML models. After deploying ML models stored in the registry, we used SageMaker Model Monitor to capture data and run processing jobs that analyze the collected data and flag any detected issues or deviations.
In the next chapter, we will focus on securing ML environments and systems using a variety of strategies and solutions. If you are serious about designing and building secure ML systems and environments, then the next chapter is for you!
