Learning Ray
Flexible Distributed Python for Machine Learning
$127.70
- Paperback
271 pages
- Release Date
3 March 2023
Summary
Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You’ll be able to use Ray to structure and run machine learning programs at scale.
Authors Max Pumperla, Edward Oakes, and Richard Liaw show you how to build machine learning applications with …
Book Details
| ISBN-13: | 9781098117221 |
|---|---|
| ISBN-10: | 1098117220 |
| Author: | Max Pumperla, Edward Oakes, Richard Liaw |
| Publisher: | O'Reilly Media |
| Imprint: | O'Reilly Media |
| Format: | Paperback |
| Number of Pages: | 271 |
| Release Date: | 3 March 2023 |
| Weight: | 440g |
| Dimensions: | 233mm x 178mm x 15mm |
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About The Author
Max Pumperla
Max Pumperla is a data science professor and software engineer located in Hamburg, Germany. He’s an active open source contributor, maintainer of several Python packages, and author of machine learning books. He currently works as software engineer at Anyscale. As head of product research at Pathmind Inc. he was developing reinforcement learning solutions for industrial applications at scale using Ray RLlib, Serve and Tune.
Edward Oakes, writing chapters 7 (data) & 9 (serving): “Edward is a software engineer and team lead at Anyscale, where he leads the development of Ray Serve and is one of the top open source contributors to Ray. Prior to Anyscale, he was a graduate student in the EECS department at UC Berkeley.”
Richard Liaw, writing chapters 6 (training) & 8 (clusters): Richard Liaw is a software engineer at Anyscale, working on open source tools for distributed machine learning. He is on leave from the PhD program at the Computer Science Department at UC Berkeley, advised by Joseph Gonzalez, Ion Stoica, and Ken Goldberg.
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