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

Gaining insight into the training infrastructure and training framework

In this section, you will learn how to gain visibility into the resource utilization of the training infrastructure and the training framework. You will also learn how to analyze and implement recommendations provided by the deep profiler capability of SageMaker Debugger.

Debugger profiler provides you with visibility into the utilization of the infrastructure running ML training jobs on SageMaker. Debugger automatically monitors system resources such as CPU, GPU, network, I/O, and memory. Additionally, Debugger collects metrics specific to the training framework such as step duration, data loading, preprocessing, and operator runtime on CPU and GPU. You can decide to profile the training job in its entirety or just portions of it to collect the necessary framework metrics.

In addition to collecting the system and framework metrics, behind the scenes, Debugger correlates these metrics automatically, which...