click to
look inside
Look inside
Manning Early Access Program (MEAP) Read chapters as they are written, get the finished eBook as soon as it’s ready, and receive the pBook long before it's in bookstores.
FREE
You can see any available part of this book for free.
Click the table of contents to start reading.

Distributed Machine Learning Patterns

Yuan Tang
  • MEAP began June 2021
  • Publication in Early 2022 (estimated)
  • ISBN 9781617299025
  • 375 pages (estimated)
  • printed in black & white

placing your order...

Don't refresh or navigate away from the page.
print book Receive a print copy shipped to your door + the eBook in Kindle, ePub, & PDF formats + liveBook, our enhanced eBook format accessible from any web browser. $34.79 $59.99 you save: $25 (42%) pBook + eBook + liveBook
Additional shipping charges may apply
FREE domestic shipping on orders of three or more print books
Distributed Machine Learning Patterns (print book) added to cart
continue shopping
go to cart

eBook Our eBooks come in Kindle, ePub, and DRM-free PDF formats + liveBook, our enhanced eBook format accessible from any web browser. $27.83 $47.99 you save: $20 (42%) 3 formats + liveBook
FREE domestic shipping on orders of three or more print books
Distributed Machine Learning Patterns (eBook) added to cart
continue shopping
go to cart

This is a really well thought out book on the problem of dealing with machine learning in a distributed environment.

Richard Vaughan
Look inside
Practical patterns for scaling machine learning from your laptop to a distributed cluster.

In Distributed Machine Learning Patterns you will learn how to:

  • Apply distributed systems patterns to build scalable and reliable machine learning projects
  • Construct machine learning pipelines with data ingestion, distributed training, model serving, and more
  • Automate machine learning tasks with Kubernetes, TensorFlow, Kubeflow, and Argo Workflows
  • Make trade offs between different patterns and approaches
  • Manage and monitor machine learning workloads at scale

Distributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters. In it, you’ll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines.

about the technology

Scaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners. Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. In this book, Kubeflow co-chair Yuan Tang shares patterns, techniques, and experience gained from years spent building and managing cutting-edge distributed machine learning infrastructure.

about the book

Distributed Machine Learning Patterns is filled with practical patterns for running machine learning systems on distributed Kubernetes clusters in the cloud. Each pattern is designed to help solve common challenges faced when building distributed machine learning systems, including supporting distributed model training, handling unexpected failures, and dynamic model serving traffic. Real-world scenarios provide clear examples of how to apply each pattern, alongside the potential trade offs for each approach. Once you’ve mastered these cutting edge techniques, you’ll put them all into practice and finish up by building a comprehensive distributed machine learning system.

about the reader

For data analysts, data scientists, and software engineers who know the basics of machine learning algorithms and running machine learning in production. Readers should be familiar with the basics of Bash, Python, and Docker.

about the author

Yuan Tang is a senior software engineer at Ant Group, where he works on AI infrastructure and AutoML platforms on Kubernetes. Yuan is co-chair of Kubeflow, top contributor to Argo Workflows, and committer on TensorFlow. He is the co-author of TensorFlow in Practice and author of the TensorFlow implementation of Dive into Deep Learning.

FREE domestic shipping on orders of three or more print books

A sound introduction to the exciting field of distributed ml for practitioners.

Pablo Roccat

I came away with a greater familiarity with distributed training ideas, problems, and solutions.

Matt Sarmiento
RECENTLY VIEWED