Book Image

Amazon SageMaker Best Practices

By : Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode
Book Image

Amazon SageMaker Best Practices

By: Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode

Overview of this book

Amazon SageMaker is a fully managed AWS service that provides the ability to build, train, deploy, and monitor machine learning models. The book begins with a high-level overview of Amazon SageMaker capabilities that map to the various phases of the machine learning process to help set the right foundation. You'll learn efficient tactics to address data science challenges such as processing data at scale, data preparation, connecting to big data pipelines, identifying data bias, running A/B tests, and model explainability using Amazon SageMaker. As you advance, you'll understand how you can tackle the challenge of training at scale, including how to use large data sets while saving costs, monitoring training resources to identify bottlenecks, speeding up long training jobs, and tracking multiple models trained for a common goal. Moving ahead, you'll find out how you can integrate Amazon SageMaker with other AWS to build reliable, cost-optimized, and automated machine learning applications. In addition to this, you'll build ML pipelines integrated with MLOps principles and apply best practices to build secure and performant solutions. By the end of the book, you'll confidently be able to apply Amazon SageMaker's wide range of capabilities to the full spectrum of machine learning workflows.
Table of Contents (20 chapters)
Section 1: Processing Data at Scale
Section 2: Model Training Challenges
Section 3: Manage and Monitor Models
Section 4: Automate and Operationalize Machine Learning

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...