Part 2 – End-to-End Machine Learning Life Cycle with SageMaker Studio

In this section of the book, you will gain a working knowledge of each SageMaker Studio component for the machine learning (ML) life cycle and how and when to apply SageMaker features in your ML use cases.

This section comprises the following chapters:

• Chapter 3, Data Preparation with SageMaker Data Wrangler
• Chapter 4, Building a Feature Repository with SageMaker Feature Store
• Chapter 5, Building and Training ML Models with SageMaker Studio IDE
• Chapter 6, Detecting ML Bias and Explaining Models with SageMaker Clarify
• Chapter 7, Hosting ML Models in the Cloud: Best Practices
• Chapter 8, Jumpstarting ML with SageMaker JumpStart and Autopilot