Summary
In this chapter, you saw how to map SageMaker capabilities to different phases of the ML life cycle. You got a quick look at important SageMaker capabilities and saw how to set up your own SageMaker environment.
This chapter further covered the advantages of using IaC/CaC (AWS CloudFormation) as well as a centrally managed catalog of IT services (AWS Service Catalog) to create data science environments at scale. The approaches discussed provide the guidance needed to reduce manual effort, provide consistency, accelerate access to model-building services, and enforce governance controls within model-building environments.
In the next chapter, you will learn more about labeling data for ML projects.
