Applied Machine Learning and High-Performance Computing on AWS : Accelerate the development of machine learning applications following architectural best practices
Language: English
Published by Packt Publishing, 2022
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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£ 71.08
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Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - 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 Amazon SageMaker Key Features:Understand the need for high-performance computing (HPC) Build, train, and deploy large ML models with billions of parameters using Amazon SageMaker Learn 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 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. What You Will Learn:Explore data management, storage, and fast networking for HPC applications Focus on the analysis and visualization of a large volume of data using Spark Train visual transformer models using SageMaker distributed training Deploy and manage ML models at scale on the cloud and at the edge Get to grips with performance optimization of ML models for low latency workloads Apply HPC to industry domains such as CFD, genomics, AV, and optimization Who this book is for: The book begins with HPC concepts, however, it expects you to have prior machine learning knowledge. This book is for ML engineers and data scientists interested in learning advanced topics on using large datasets for training large models using distributed training concepts on AWS, deploying models at scale, and performance optimization for low latency use cases. Practitioners in fields such as numerical optimization, computation fluid dynamics, autonomous vehicles, and genomics, who require HPC for applying ML models to applications at scale will also find the book useful. …
Seller Inventory # 9781803237015
- Title
- Applied Machine Learning and High-Performance Computing on AWS : Accelerate the development of machine learning applications following architectural best practices
- Author
- Mani Khanuja
- Publisher
- Packt Publishing
- Publication year
- 2022
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1803237015
- ISBN 13
- 9781803237015
- Item weight
- 712 grams
- Dimensions
- 235x191x21 mm
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 Amazon SageMaker.
Key Features
- Understanding the need for High Performance Computing (HPC).
- Build, train, and deploy large ML models with billions of parameters using Amazon 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 emerging 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
Who This Book Is For
The book begins with HPC concepts, however, expects you to have prior machine learning knowledge. This book is for ML engineers and Data Scientists, interested in learning advanced topics on using large dataset for training large models using distributed training concepts on AWS, followed by deploying models at scale and performance optimization for low latency use cases. This book is also beneficial for Practitioners in fields such as numerical optimization, computation fluid dynamics, autonomous vehicles, and genomics, who require HPC for applying ML models to applications at scale.
Table of Contents
- High Performance Computing Fundamentals
- Data Management and Transfer
- Compute and Networking
- Data Storage
- Data Analysis
- Distributed Training of Machine Learning Models
- Deploying Machine Learning Models at Scale
- Optimizing and Managing Machine Learning Models for Edge Deployment
- Performance Optimization for Real-time Inference on Cloud
- Visualization
- Computational Fluid Dynamics
- Genomics
- Autonomous Vehicles
- Numerical Optimization
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"About the title" may belong to another edition of this title.
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