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

Managing models using the Amazon SageMaker model registry

An introduction to the Amazon SageMaker model registry was included in the section titled Amazon SageMaker model registry. This was done in order to explain the high-level architecture and features that are important to consider when choosing a model registry implementation. In this section, we'll dive deeper into the Amazon SageMaker model registry by covering the process and best practice guidance when setting up and using SageMaker's model registry.

SageMaker's model registry includes the model registry, as well as model groups and model packages. Each model group contains model versions, or model packages, related to the same ML problem. Each model package represents a specific version of a model and includes metadata associated with that version. The SageMaker model registry APIs are used when interacting with the SageMaker model registry, and those APIs can also be called through any of the following...