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Distributed Machine Learning Patterns from Manning Publications by Yuan Tang https://bit.ly/2RKv8Zo
| Date | Stars |
|---|---|
| 2026-07-24 | 510 |
| 2026-07-25 | 510 |
| 2026-07-28 | 510 |
| 2026-07-30 | 510 |
| 2026-07-31 | 510 |
| 2026-08-06 | 512 |
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+2 stars today
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# Distributed Machine Learning Patterns <a href="https://terrytangyuan.github.io/about"><img src="https://raw.githubusercontent.com/terrytangyuan/terrytangyuan/master/imgs/followers.svg" alt="Followers"></a> <a href="https://www.linkedin.com/in/terrytangyuan"><img src="https://raw.githubusercontent.com/terrytangyuan/terrytangyuan/master/imgs/linkedin.svg" alt="LinkedIn"></a> <a href="https://bsky.app/profile/terrytangyuan.xyz"><img src="https://raw.githubusercontent.com/terrytangyuan/terrytangyuan/master/imgs/bluesky.svg" alt="Bluesky"></a> <a href="https://x.com/TerryTangYuan"><img src="https://raw.githubusercontent.com/terrytangyuan/terrytangyuan/master/imgs/twitter.svg" alt="Twitter"></a> <a href="https://fosstodon.org/@terrytangyuan"><img src="https://raw.githubusercontent.com/terrytangyuan/terrytangyuan/master/imgs/mastodon.svg" alt="Mastodon"></a> <a href="https://substack.com/@terrytangyuan"><img src="https://raw.githubusercontent.com/terrytangyuan/terrytangyuan/master/imgs/substack.svg" alt="Substack"></a>  This repository contains references and code for the book *Distributed Machine Learning Patterns* from [Manning Publications](https://bit.ly/2RKv8Zo) by [Yuan Tang](https://github.com/terrytangyuan). :fire: **[Korean](images/korean-cover.jpg) and [Chinese](images/chinese-cover.pdf) versions are available from Tsinghua University Press and Hanbit Media!** [Manning](https://bit.ly/2RKv8Zo), [Amazon](https://www.amazon.com/dp/1617299022/), [Barnes & Noble](https://www.barnesandnoble.com/w/distributed-machine-learning-patterns-yuan-tang/1140209010), [Powell’s]( https://www.powells.com/book/distributed-machine-learning-patterns-9781617299025), [Bookshop](https://bookshop.org/p/books/distributed-machine-learning-patterns-yuan-tang/17491200) In *Distributed Machine Learning Patterns* you will learn how to: * Apply patterns to build scalable and reliable machine learning systems. * Construct machine learning pipelines with data ingestion, distributed training, model serving, and more. * Automate machine learning tasks with [Kubernetes](https://kubernetes.io/), [TensorFlow](https://www.tensorflow.org/), [Kubeflow](https://www.kubeflow.org/), and [Argo Workflows](https://argoproj.github.io/argo-workflows/). * Make trade off decisions between different patterns and approaches. * Manage and monitor machine learning workloads at scale. This book teaches you how to take machine learning models from your personal laptop to large distributed clusters. You’ll explore key concepts and patterns behind successful distributed machine learning systems, and learn technologies like TensorFlow, Kubernetes, Kubeflow, and Argo Workflows directly from a key maintainer and contributor. Real-world scenarios, hands-on projects, and clear, practical advice DevOps techniques and let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines. ## About the topic Scaling up models from personal 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, 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 trad
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Yuan Tang · Red Hat · United States
99
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d74b9bed0b3a7a64, topic:mlops, topic:kubeflow
matched fp:d74b9bed0b3a7a64, topic:kubernetes
matched fp:d74b9bed0b3a7a64, topic:tensorflow, readme:distributed training
matched fp:d74b9bed0b3a7a64, topic:book