Section 1: Processing Data at Scale

This section sets the foundation for the rest of the book with an overview of Amazon SageMaker capabilities, a review of technical requirements, and insights on setting up the data science environment on AWS. This section then addresses the challenges involved in labeling and preparing large volumes of data. You will learn how to apply appropriate Amazon SageMaker capabilities and related services to derive features from raw data and persist features for reuse. Further, you will also learn how to persist features in a centralized repository to share across multiple ML projects.

This section comprises the following chapters:

• Chapter 1, Amazon SageMaker Overview
• Chapter 2, Data Science Environments
• Chapter 3, Data Labeling with Amazon SageMaker Ground Truth
• Chapter 4, Data Preparation at Scale Using Amazon SageMaker Data Wrangler and Processing
• Chapter 5, Centralized Feature Repository with Amazon SageMaker Feature Store...