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Distributed Machine Learning Patterns
Practical patterns for scaling machine learning from your laptop to a distributed cluster.
In Distributed Machine Learning Patterns you will learn how
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.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
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 currently a founding engineer at Akuity. Previously he was a senior software engineer at Alibaba Group, building AI infrastructure and AutoML platforms on Kubernetes. Yuan is co-chair of Kubeflow, maintainer of Argo, TensorFlow, XGBoost, and Apache MXNet. He is the co-author of TensorFlow in Practice and author of the TensorFlow implementation of Dive into Deep Learning.
In Distributed Machine Learning Patterns you will learn how
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.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
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 currently a founding engineer at Akuity. Previously he was a senior software engineer at Alibaba Group, building AI infrastructure and AutoML platforms on Kubernetes. Yuan is co-chair of Kubeflow, maintainer of Argo, TensorFlow, XGBoost, and Apache MXNet. He is the co-author of TensorFlow in Practice and author of the TensorFlow implementation of Dive into Deep Learning.
248 pages, Paperback
Published January 2, 2024
9 people are currently reading
58 people want to read
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Displaying 1 - 7 of 7 reviews
January 21, 2024
Yuan Tang's 'Distributed Machine Learning Patterns' emerges as a crucial text in understanding the intricate landscape of contemporary machine learning challenges, which are inherently distributed and demand reproducibility. This book stands out for its adept blend of theoretical insights and practical applications, making it an indispensable resource for newcomers and seasoned practitioners. Tang demystifies complex concepts and offers a comprehensive guide to implementing distributed machine learning systems. Readers seeking a deep dive into the subject will find no better starting point than this book, as it meticulously bridges the gap between theory and real-world application in distributed machine learning.
January 3, 2024
The book is a must to read if you work in Machine Learning and want to understand how to release your model in production.
The author uses clear and simple language to describe the entire process, the diagram in the book makes this very easy to follow and understand.
It is a must to read for everyone working on the space Machine Learning.
The author uses clear and simple language to describe the entire process, the diagram in the book makes this very easy to follow and understand.
It is a must to read for everyone working on the space Machine Learning.
March 23, 2024
Distributed Machine Learning Patterns
This was an okay book on machine learning patterns.
I got some good points from it.
Not as good as some books I have read on this topic, but a good enough insight.
Check it out if this interests you.
Also, the cover of the book has a dude with the world's longest pipe - why
3.6/5
This was an okay book on machine learning patterns.
I got some good points from it.
Not as good as some books I have read on this topic, but a good enough insight.
Check it out if this interests you.
Also, the cover of the book has a dude with the world's longest pipe - why
3.6/5
January 19, 2024
A few days ago, I started reading this book, and I loved that it covers patterns for construction and the operation of ML solutions. Chapter 7 was my favorite because it adequately synthesizes the architecture of ML solutions. Highly recommended!
September 7, 2024
The content is useful, but the author repeats themself over and over. The writing is also unclear, even when the topic is straightforward. Additionally, the book goes into machine learning basics that don't seem necessary or relevant for a book focused on distributed ML patterns.
August 12, 2024
I wish it there was more "distributed ML in there"
January 9, 2024
Great book!
It explains the concepts that are not frequently discussed somewhere else, in a good level of detail.
It explains the concepts that are not frequently discussed somewhere else, in a good level of detail.
Displaying 1 - 7 of 7 reviews