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Key benefits

Accelerate model training and interference with order-of-magnitude time reduction

Learn state-of-the-art parallel schemes for both model training and serving

A detailed study of bottlenecks at distributed model training and serving stages

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.

Who is this book 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.

What you will learn

Deploy distributed model training and serving pipelines

Get to grips with the advanced features in TensorFlow and PyTorch

Mitigate system bottlenecks during in-parallel model training and serving

Discover the latest techniques on top of classical parallelism paradigm

Explore advanced features in Megatron-LM and Mesh-TensorFlow

Use state-of-the-art hardware such as NVLink, NVSwitch, and GPUs

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

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AI Assistant (beta) to help accelerate your learning

Frequently bought together

€35.99

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Distributed Machine Learning with Python

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Total € 104.97

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Amazon verified reviews

Baron C. Jun 03, 2022

This book covers an area that isn't taught much, and especially not in academia. Distributed ML is going to be how you get the performance you need. Python is naturally synchronous and this book teaches how to scale up ML to be asynchronous (a necessary addition to anyone's toolset). It also does a great job in covering the pros and cons of each approach. Understanding why you do something is paramount in tech as explaining tradeoffs is a critical part of the job.At a high level, this book covers data parallelism, model synchronization, parallel training, bottlenecks and solutions, pipeline parallelism, parallel serving, elastic model training, and various other ways to speed up the process. You get the picture from the 30k foot view and in great detail.

Amazon Verified review

Haoran YU May 26, 2022

An awesome book for ML engineers in cloud computing for practices of the machine learning algorithms on modern distributed computing platforms. I find this book a great source of information regarding for algorithm and system design! Highly Recommend!!

@maxgoff May 24, 2022

Although distributed computing has become de rigueur in most modern web applications, the fact remains that most training and reference materials for ML/AI programming still focus on single node architectures. One undeniable trend is the growing girth of data required to train some of the most interesting models emerging today. In order to rapidly innovate and compete, distributed ML will become table stakes in the near future as we move forward.If you write ML/AI code, implement smart data pipelines, architect systems in order to scale or simply want to learn techniques beyond the common core ML/AI training available, this book is a must-have for your shelf. Wang covers a lot of territory and does so clearly with excellent examples. He also provides the technical foundation for the WHY.As more data and processing capabilities accumulate at the edge, the exponentially expanding universe of data processing demands distributed computing. Machine Learning must follow a distributed pattern if it is to continue to provide value. Wang's text provides a solid foundation and reference point.Distributed computing is awesome. We use distributed computing applications every day. Wang's text provides the lessons you will need to ensure that modern ML innovations will utilize resources with much greater productivity. Time is our most precious resource. Distributed Machine Learning with Python will save you LOTS of it!

Kenan May 14, 2022

As someone who works with large NLP models everyday, I found this book extremely helpful in industry settings. Not only it provides detailed explanation on different parallel training techniques with clear and simple design-flow pictures, the book also contains code snippets and error messages. One thing I love most about this book is that it takes a very practical perspective. The discussion on outputs and errors with screenshots just makes the process of re-implementing those techniques so much easier for me!I would recommend this book to all researchers and young ML engineers 100%!

Hitesh Hinduja Aug 11, 2022

Interesting book with a need of hour in today's age of data. Must read for all the distributed systems enthusiasts.

Distributed Machine Learning with Python: Accelerating model training and serving with distributed systems

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