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

Data Storage

Data storage in the cloud has become very common, not just for personal usage but also for business, computational, and application purposes as well. On the personal side, cloud storage is provided by well-known companies, ranging from a free usage tier of a few Gigabytes (GBs), to pay monthly or yearly plans for Terabytes (TBs) of data. These services are well integrated with applications on mobile devices, enabling users to store thousands of pictures, videos, songs, and other types of files.

For applications requiring high-performance computations, cloud data storage plays an even bigger role. For example, training Machine Learning (ML) models over large datasets generally requires algorithms to run in a distributed fashion. If data is stored on the cloud, then it makes it much easier and more efficient for the ML platform to partition the data stored in the cloud and make these separate partitions available to the distributed components of the model training job...