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 6: Training and Tuning at Scale

Machine learning (ML) practitioners face multiple challenges when training and tuning models at scale. Scale challenges come in the form of high volumes of training data and increased model size and model architecture complexity. Additional challenges come from having to run a large number of tuning jobs to identify the right set of hyperparameters and keeping track of multiple experiments conducted with varying algorithms for a specific ML objective. Scale challenges lead to long training times, resource constraints, and increased costs. This can reduce the productivity of teams, and potentially create a bottleneck for ML projects.

Amazon SageMaker provides managed distributed training and tuning capabilities to improve training efficiency, and capabilities to organize and track ML experiments at scale. SageMaker enables techniques such as streaming data into algorithms by using pipe mode for training with data at scale and Managed Spot Training...