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Applied Machine Learning and High-Performance Computing on AWS
Accelerate the development of machine learning applications following architectural best practices
by Mani Khanuja and others
$91.75
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Paperback
Pub: 30/12/2022
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… more ML models with billions of parameters using Amazon SageMaker
Learn best practices and architectures for implementing ML at scale using HPC
Book DescriptionMachine 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 forThe 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. less
Key Features
Understand the need for high-performance computing (HPC)
Build, train, and deploy large… more ML models with billions of parameters using Amazon SageMaker
Learn best practices and architectures for implementing ML at scale using HPC
Book DescriptionMachine 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 forThe 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. less
- ISBN:
- 9781803237015
- 9781803237015
- Category:
- Machine learning
- Format:
- Paperback
- Publication Date:
- 30-12-2022
- Publisher:
- Packt Publishing Limited
- Country of origin:
- United Kingdom
- Pages:
- 382
- Dimensions:
- 93x75mm
This title is in stock with our Australian supplier and should arrive at our Sydney warehouse within 2 - 3 weeks of you placing an order.
Once received into our warehouse we will despatch it to you with a Shipping Notification which includes online tracking.
Please check the estimated delivery times below for your region, for after your order is despatched from our warehouse:
ACT Metro: 2 working days
NSW Metro: 2 working days
NSW Rural: 2-3 working days
NSW Remote: 2-5 working days
NT Metro: 3-6 working days
NT Remote: 4-10 working days
QLD Metro: 2-4 working days
QLD Rural: 2-5 working days
QLD Remote: 2-7 working days
SA Metro: 2-5 working days
SA Rural: 3-6 working days
SA Remote: 3-7 working days
TAS Metro: 3-6 working days
TAS Rural: 3-6 working days
VIC Metro: 2-3 working days
VIC Rural: 2-4 working days
VIC Remote: 2-5 working days
WA Metro: 3-6 working days
WA Rural: 4-8 working days
WA Remote: 4-12 working days
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