English | 2022 | ISBN: ‎ 1803237015 | 382 pages | True PDF EPUB | 38.19 MB

Build, train, and deploy large machine learning models at scale in various domains such as computational fluid dynamics, genomics, autonomous vehicles, and numerical optimization using SageMaker.

Key Features

Understanding the need for High Performance Computing (HPC).

Build, train, and deploy large ML models with billions of parameters using SageMaker.

Best practices and architectures for implementing ML at scale using HPC.

Book Description

Machine Learning (ML) and High Performance Computing (HPC) on AWS run compute intensive workloads across industries and emeg applications. It's use cases can be linked to various verticals like computational fluid dynamics (CFD), genomics, and autonomous vehicles.

The book provides end-to-end guidance starting from HPC concepts for storage and networking. It then goes deeper into part 2, with working examples on how to process large datasets using SageMaker Studio and EMR, build, train, and deploy large models using distributed training. It also covers 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 will be able to build, train, and deploy your own large scale ML application, using HPC on AWS, following the industry best practices and addressing the key pain points encountered in the application life cycle.

What you will learn

Data management, storage, and fast networking for HPC applications

Analysis and visualization of a large volume of data using Spark

Train visual transformer model using SageMaker distributed training

Deploy and manage ML models at scale on cloud and at edge

Performance optimization of ML models for low latency workloads

Apply HPC to industry domains like CFD, genomics, AV, and optimization