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Guanhua Wang Distributed Machine Learning with Python (Paperback) (UK IMPORT)

Description: Further DetailsTitle: Distributed Machine Learning with PythonCondition: NewSubtitle: Accelerating model training and serving with distributed systemsEAN: 9781801815697ISBN: 9781801815697Publisher: Packt Publishing LimitedFormat: PaperbackRelease Date: 07/22/2022Description: Build and deploy an efficient data processing pipeline for machine learning model training in an elastic, in-parallel model training or multi-tenant cluster and cloudKey FeaturesAccelerate model training and interference with order-of-magnitude time reductionLearn state-of-the-art parallel schemes for both model training and servingA detailed study of bottlenecks at distributed model training and serving stagesBook Description Reducing time cost in machine learning leads to a shorter waiting time for model training and a faster model updating cycle. Distributed machine learning enables machine learning practitioners to shorten model training and inference time by orders of magnitude. With the help of this practical guide, you'll be able to put your Python development knowledge to work to get up and running with the implementation of distributed machine learning, including multi-node machine learning systems, in no time. You'll begin by exploring how distributed systems work in the machine learning area and how distributed machine learning is applied to state-of-the-art deep learning models. As you advance, you'll see how to use distributed systems to enhance machine learning model training and serving speed. You'll also get to grips with applying data parallel and model parallel approaches before optimizing the in-parallel model training and serving pipeline in local clusters or cloud environments. By the end of this book, you'll have gained the knowledge and skills needed to build and deploy an efficient data processing pipeline for machine learning model training and inference in a distributed manner.What you will learnDeploy distributed model training and serving pipelinesGet to grips with the advanced features in TensorFlow and PyTorchMitigate system bottlenecks during in-parallel model training and servingDiscover the latest techniques on top of classical parallelism paradigmExplore advanced features in Megatron-LM and Mesh-TensorFlowUse state-of-the-art hardware such as NVLink, NVSwitch, and GPUsWho this book is for This book is for data scientists, machine learning engineers, and ML practitioners in both academia and industry. A fundamental understanding of machine learning concepts and working knowledge of Python programming is assumed. Prior experience implementing ML/DL models with TensorFlow or PyTorch will be beneficial. You'll find this book useful if you are interested in using distributed systems to boost machine learning model training and serving speed.Language: EnglishCountry/Region of Manufacture: GBItem Height: 93mmItem Length: 75mmAuthor: Guanhua WangGenre: Computing & InternetRelease Year: 2022 Missing Information?Please contact us if any details are missing and where possible we will add the information to our listing.

Price: 68.49 USD

Location: GU14 0GT

End Time: 2024-12-20T23:33:06.000Z

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Guanhua Wang Distributed Machine Learning with Python (Paperback) (UK IMPORT)

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Book Title: Distributed Machine Learning with Python

Title: Distributed Machine Learning with Python

Subtitle: Accelerating model training and serving with distributed systems

EAN: 9781801815697

ISBN: 9781801815697

Release Date: 07/22/2022

Release Year: 2022

Country/Region of Manufacture: GB

Item Height: 93mm

Item Length: 75mm

Genre: Computing & Internet

Publisher: Packt Publishing, The Limited

Subject: Data Modeling & Design, Natural Language Processing, General, Databases / Data Warehousing

Publication Year: 2022

Number of Pages: 284 Pages

Language: English

Publication Name: Distributed Machine Learning with Python : Accelerating Model Training and Serving with Distributed Systems

Type: Textbook

Author: Guanhua Wang

Subject Area: Mathematics, Computers

Format: Trade Paperback

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