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


In this chapter, you reviewed the five pillars – operational excellence, security, reliability, performance, and cost optimization – that make up the Well-Architected Framework. You then dove into the best practices for each of these pillars, with an eye to applying these best practices to ML workloads. You learned how to use the SageMaker capabilities with related AWS services to build well-architected ML workloads on AWS.

As you architect your ML applications, you typically must make trade-offs between the pillars depending on your organization's priorities. For example, when getting started with ML, cost-optimization may not be at the top of your mind but establishing operational standards may be important. However, as the number of ML workloads scale, cost-optimization could become an important consideration. By applying the best practices you learned in this chapter, you can architect and implement ML applications that meet your organization&apos...