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

Chapter 5: Centralized Feature Repository with Amazon SageMaker Feature Store

Let's begin with the basic questions – what is a feature store and why is it necessary? A feature store is a repository that persists engineered features. A lot of time goes into feature engineering, sometimes involving multi-step data processing pipelines executed over hours of compute time. ML models depend on these engineered features that often come from a variety of data sources. A feature store accelerates this process by reducing repetitive data processing that is required to convert raw data into features. A feature store not only allows you to share engineered features during model-building activities, but also allows consistency in using engineered features for inference.

Amazon SageMaker Feature Store is a managed repository with capabilities to store, update, retrieve, and share features. SageMaker Feature Store provides the ability to reuse the engineered features in two different...