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Eric Liang

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Robert Nishihara

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Training One Million Machine Learning Models in Record Time with Ray

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Introducing Anyscale: The Future Is Distributed

Michael Galarnyk

Richard Liaw

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PyTorch + Ray

Getting Started with Distributed Machine Learning with PyTorch and Ray

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Anyscale Announces $40M in Series B Funding Led by NEA

Ion Stoica

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The Infinite Laptop

Founders of Open Source Project Ray Launch Anyscale with $20.6M in Funding to Democratize Distributed Programming

Exoshuffle is a new architecture for building distributed shuffle that is simple, flexible, and achieves high performance. Building upon this architecture and Ray, Exoshuffle-CloudSort is now the most cost-efficient way to sort 100TB of data on the public cloud, using only $97 worth of cloud resources, or $0.97 per terabyte. This is 33% more cost-efficient than the previous world record, set by Apache Spark in 2016, and 15% cheaper when factoring in decreasing hardware costs.

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Ray breaks the $1/TB barrier as the world’s most cost-efficient sorting system

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Biolexis Boosts Their New AI/ML Drug Discovery Platform Using Anyscale’s Fully-Managed Ray Platform

Ray For Success

Four Reasons Why Leading Companies Are Betting On Ray

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Ray Summit, the Industry Event for Scalable AI, Unveils New Innovations in Ray and the Anyscale Platform; Features Speakers from Uber, IBM, OpenAI, Shopify, Dow and Numerous Global Organizations

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From the creators of Ray, Anyscale is a framework for building machine learning applications at any scale originating from the UC Berkeley RISELab.

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Scalable AI and Python

Posts by Robert Nishihara

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