Book Image

Applied Machine Learning and High-Performance Computing on AWS

By : Mani Khanuja, Farooq Sabir, Shreyas Subramanian, Trenton Potgieter
Book Image

Applied Machine Learning and High-Performance Computing on AWS

By: Mani Khanuja, Farooq Sabir, Shreyas Subramanian, Trenton Potgieter

Overview of this book

Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles. This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you’ll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases. By the end of this book, you’ll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle.
Table of Contents (20 chapters)
1
Part 1: Introducing High-Performance Computing
6
Part 2: Applied Modeling
13
Part 3: Driving Innovation Across Industries

Preface

Applied machine learning (ML) and high-performance computing (HPC) have been integral to tackling the world’s most complex problems, including genomics, autonomous vehicles, computational fluid dynamics, and numerical optimization. Due to the high computing power required for these applications, HPC was once only used by large organizations that could afford it. Now, it is available for use by everyone, from individual research groups to start-ups and big enterprises, using Amazon Web Services (AWS) cloud technology.

This book provides a complete step-by-step explanation of the essential concepts with practical examples. You will begin by exploring virtually unlimited infrastructure and fast networking for scalable HPC on AWS, including an overview of the relevant tools and technologies. You’ll learn how to develop large-scale ML applications using HPC on AWS, you will understand the various architectural components, and you’ll learn about performance optimization, with hands-on application to real-world use cases in various domains.

By the end of this book, you will be able to build and deploy your own large-scale ML applications using HPC on AWS, following the industry best practices and addressing the key pain points encountered in the application life cycle.