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

Summary

In this chapter, we talked about various aspects of data management, including data governance and compliance with the legal requirements of federal and regional authorities of the country where the data resides. We also discussed that in order to build HPC applications on the cloud, we need to have data on the cloud, and looked at the challenges of transferring this data to the cloud. In order to mitigate these challenges, we can use the managed AWS data transfer services, and in order to select which service to use for your application, we then discussed the elements of building a data strategy.

We then took an example of how we can transfer petabyte-scale data to the cloud in order to understand the concepts involved in a data transfer strategy. Finally, we did a deep dive on various AWS data transfer services for both online and offline data transfer based on your network bandwidth, connectivity, type of application, speed of data transfer, and location of your data...