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

Chapter 7: Profile Training Jobs with Amazon SageMaker Debugger

Training machine learning (ML) models involves experimenting with multiple algorithms, with their hyperparameters typically crunching through large volumes of data. Training a model that yields optimal results is both a time- and compute-intensive task. Improved training time yields improved productivity and reduces overall training costs.

Distributed training, as we discussed in Chapter 6, Training and Tuning at Scale, goes a long way in achieving improved training times by using a scalable compute cluster. However, monitoring training infrastructure to identify and debug resource bottlenecks is not trivial. Once a training job has been launched, the process becomes non-transparent, and you don't have much visibility into the model training process. Equally non-trivial is real-time monitoring to detect sub-optimal training jobs and stop them early to avoid wasting training time and resources.