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, we discussed the benefits of using multiple accounts to manage and operate machine learning workloads that use Amazon SageMaker across the ML Lifecycle. We also looked at common patterns for account isolation across the ML Lifecycle. Finally, we focused specifically on the SageMaker features that are most often used across accounts, and the considerations you should be aware of when architecting and building end-to-end machine learning solutions.

This chapter wraps up the book where we covered best practices for SageMaker across features spanning the machine learning lifecycle of data preparation, model training, and operations. In this book, we discussed best practices, as well as considerations, that you can draw on when creating your own projects. We used an example use case, using open weather data to demonstrate the concepts throughout the chapters of the book. This was done so you can get hands-on with the concepts and practices discussed. We hope...