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Applied Machine Learning and High-Performance Computing on AWS
Applied Machine Learning and High-Performance Computing on AWS

Applied Machine Learning and High-Performance Computing on AWS: Accelerate the development of machine learning applications following architectural best practices

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Profile Icon Mani Khanuja Profile Icon Farooq Sabir Profile Icon Shreyas Subramanian Profile Icon Trenton Potgieter
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$37.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (11 Ratings)
eBook Dec 2022 382 pages 1st Edition
eBook
$37.99
Paperback
$46.99
Subscription
Free Trial
Renews at $12.99p/m
Arrow left icon
Profile Icon Mani Khanuja Profile Icon Farooq Sabir Profile Icon Shreyas Subramanian Profile Icon Trenton Potgieter
Arrow right icon
$37.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (11 Ratings)
eBook Dec 2022 382 pages 1st Edition
eBook
$37.99
Paperback
$46.99
Subscription
Free Trial
Renews at $12.99p/m
eBook
$37.99
Paperback
$46.99
Subscription
Free Trial
Renews at $12.99p/m

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Key benefits

  • 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

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.

Who is this book 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.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 30, 2022
Length: 382 pages
Edition : 1st
Language : English
ISBN-13 : 9781803244440
Category :

What do you get with eBook?

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Product Details

Publication date : Dec 30, 2022
Length: 382 pages
Edition : 1st
Language : English
ISBN-13 : 9781803244440
Category :

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Machine Learning Techniques for Text
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Applied Machine Learning and High-Performance Computing on AWS
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.7
(11 Ratings)
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tt0507 Mar 18, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a wonderful and very practical guide to applying machine learning (ML) on AWS. The book details important high-performance computing (HPC) concepts such as computing, networking, and data storage. The book also provides detailed explanations and examples of data analysis, distributed training, model deployment and optimization, and data visualization. The book acts as a great resource for anyone who uses AWS for ML applications. It dives deep into several components of AWS and provides detailed instructions and examples on how to use develop ML applications on AWS as a whole instead of just using several services.
Amazon Verified review Amazon
ojo123 Feb 08, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I was asked to review the "Applied Machine Learning on AWS" text and here is my review. The book provides in-depth coverage of the AWS platform and how ML applications can be built and run on it. It discusses data storage on AWS using Amazon Simple Storage Service (S3) and AWS Lake Formation with the pros and cons of each storage service. It demonstrates how to load and preprocess data using Pyspark and Pandas Dataframe on AWS. It explains how to build models using SageMaker's built-in PyTorch and then goes on to explain how to perform distributed training of machine learning models on AWS to speed up the training process. Lastly, it explains how to deploy machine models on AWS. The book is a great addition for anyone interested in building and running machine on the cloud, especially on the AWS platform
Amazon Verified review Amazon
Amazon Customer Jan 10, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Recommended reading, to understand HPC applications, using Amazon SageMaker, which is an IDE to build, train and deploy machine learning applications.
Amazon Verified review Amazon
Parth Acharya Oct 08, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book "Applied Machine Learning and High-Performance Computing on AWS" provides a comprehensive guide to building, training, and deploying large-scale machine learning models on AWS, covering topics like HPC fundamentals, distributed training, deployment at scale, optimizing models, and applying ML to domains like CFD and genomics.Ideal for data scientists new to AWS or looking to leverage AWS for ML, it gives practical guidance and detailed examples using services like SageMaker and EC2 to build real-world ML solutions.
Amazon Verified review Amazon
Devanshu Mar 21, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book provides in-depth coverage of the AWS platform and how machine learning applications can be built and run on it. It discusses data storage on AWS using Amazon Simple Storage Service (S3) and AWS Lake Formation, with the pros and cons of each storage service. It demonstrates how to load and preprocess data using Pyspark and Pandas data frame, and then explains how to build models using SageMaker's built-in PyTorch. Lastly, it explains how to deploy machine models on AWS. This is a great addition for anyone interested in building and running machine learning applications on the cloud, especially on the AWS platform.
Amazon Verified review Amazon
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