Learning Ray: Flexible Distributed Python for Machine Learning

Name: Learning Ray: Flexible Distributed Python for Machine Learning
Brand: O'Reilly Media
SKU: 9781098117221
Price: 4391.0 INR
Availability: InStock

by Max Pumperla

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ISBN13: 9781098117221
Binding: Paperback
Subject: N/A
Publisher: O'Reilly Media
Publisher Imprint: O'Reilly Media
Publication Date: March 21, 2023
Pages: 250
Original Price: USD 65.99
Language: English
Edition: N/A
Item Weight: 454 grams
BISAC Subject(s): Data Science / Data Analytics and Languages / Python

About The Book

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 Ray. You'll understand how Ray fits into the current landscape of machine learning tools and discover how Ray continues to integrate ever more tightly with these tools. Distributed computation is hard, but by using Ray you'll find it easy to get started.

Learn how to build your first distributed applications with Ray Core
Conduct hyperparameter optimization with Ray Tune
Use the Ray RLlib library for reinforcement learning
Manage distributed training with the Ray Train library
Use Ray to perform data processing with Ray Datasets
Learn how work with Ray Clusters and serve models with Ray Serve
Build end-to-end machine learning applications with Ray AIR

About The Author

Liaw, Richard: - RIchard Liaw (rliaw@berkeley.edu), 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.

Oakes, Edward: - Edward Oakes (ed.nmi.oakes@gmail.com), 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."

Pumperla, Max: - 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.

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