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Clark Zinzow

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Ray Datasets for large-scale machine learning ingest and scoring

Waleed Kadous

3rdGenTasks andActors

Deep Dive: Data Ingest in a Third Generation ML Architecture

Sang Cho

Stephanie Wang

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Data Processing Support in Ray

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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Training One Million Machine Learning Models in Record Time with Ray

BlogBiolexis on Anyscale (4)

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

Anyscale logo (webinar thumbnail)

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 Clark Zinzow

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