Chapter 8: Building a Data Science Environment Using AWS ML Services

While some organizations choose to build machine learning (ML) platforms on their own using open source technologies, many other organizations prefer to use fully managed ML services as the foundation for their ML platforms. In this chapter, we will focus on the fully managed ML services offered by AWS. Specifically, you will learn about Amazon SageMaker, a fully managed ML service, and other related services for building a data science environment for data scientists. We will cover specific SageMaker components such as SageMaker Notebook, SageMaker Studio, SageMaker Training Service, and SageMaker Hosting Service. We will also discuss the architecture pattern for building a data science environment, and we will provide a hands-on exercise in building a data science environment.

After completing this chapter, you will be familiar with Amazon SageMaker, AWS CodeCommit, and Amazon ECR and be able to use these services...