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

Why do we need HPC?

According to Statista, the rate of growth of data globally is forecast to increase rapidly and reached 64.2 zettabytes in 2020. By 2025, the volume of data is estimated to grow to more than 180 zettabytes. Due to the COVID-19 pandemic, data growth in 2020 reached a new high as more people were learning online and working remotely from home. As data is continuously increasing, the need to be able to analyze and process it also increases. This is where HPC is a useful mechanism. It helps organizations to think beyond their existing capabilities and explore possibilities with advanced computing technologies. Today HPC applications, which were once confined to large enterprises and academia, are trending across a wide range of industries. Some of these industries include material sciences, manufacturing, product quality improvement, genomics, numerical optimization, computational fluid dynamics, and many more. The list of applications for HPC will continue to increase, as cloud infrastructure is making it accessible to more organizations irrespective of their size, while still optimizing cost, helping to innovate faster and gain a competitive advantage.

Before we take a deeper look into doing HPC on the cloud, let’s understand the limitations of running HPC applications on-premises, and how we can overcome them by using specialized HPC services provided by the cloud.