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

Creating feature groups

In Amazon SageMaker Feature Store, features are stored in a collection called a feature group. A feature group, in turn, is composed of records of features and feature values. Each record is a collection of feature values, identified by a unique RecordIdentifier value. Every record belonging to a feature group will use the same feature as RecordIdentifier. For example, the record identifier for the feature store created for the weather data could be parameter_id or location_id. Think of RecordIdentifier as a primary key for the feature group. Using this primary key, you can query feature groups for the fast lookup of features. It's also important to note that each record of a feature group must, at a minimum, contain a RecordIdentifier and an event time feature. The event time feature is identified by EventTimeFeatureName when a feature group is set up. When a feature record is ingested into a feature group, SageMaker adds three features – is_deleted...