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)
1
Section 1: Processing Data at Scale
7
Section 2: Model Training Challenges
10
Section 3: Manage and Monitor Models
15
Section 4: Automate and Operationalize Machine Learning

Chapter 13:Well-Architected Machine Learning with Amazon SageMaker

When running workloads in the cloud, you want to make sure that the workload is architected correctly to take advantage of all that the cloud can offer. AWS Well-Architected Framework helps you with this, by providing a formal approach for learning best practices across five critical pillars applicable to any workload deployed to AWS. The pillars are operational excellence, security, reliability, performance efficiency, and cost optimization.

The framework provides guidance on how to improve your architecture and make trade-offs between the pillars both during the initial development and continued updates of the workload. While you can use Well-Architected Framework to evaluate your workload from a general technology perspective, while building machine learning (ML) applications, it would be great to have focused guidance across the five pillars specific to ML. AWS Machine Learning Lens provides this focused guidance...