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Anyscale

anyscale
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Decks

How Ray and Anyscale Make it Easy to do Massive-scale ML on Aerial Imagery (Richard Decal, Dendra Systems)
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210
Responsible AI at Scale (Sarah Bird, Microsoft)
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170
Data Processing on Ray (SangBin Cho, Anyscale)
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140
W&B Tables and Ray (Chris Van Pelt, Weights & Biases)
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170
Per-epoch Shuffling Data Loader: Mix It Up As You Train! (Clark Zinzow, Anyscale)
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370
Scaling Interactive Data Science with Modin and Ray (Devin Petersohn, UC Berkeley))
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120
Using Reinforcement Learning to Optimize IAP Offer Recommendations in Mobile Games (Emiliano Castro, Vinicius Alves, & Felipe Antunes, Wildlife Studios)
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390
Human-in-the-Loop Reinforcement Learning (Pieter Abbeel, UC Berkeley | Covariant | The Robot Brains Podcast)
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300
Anyscale Demo: Machine Learning Application from Dev to Prod (Edward Oakes, Anyscale)
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270
Making Distributed Computing Easy (Ion Stoica, Anyscale)
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100
State of Ray (Robert Nishihara, Anyscale)
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150
Petabyte Scale Datalake Table Management with Ray, Arrow, Parquet, and S3 (Patrick Ames, Amazon)
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990
The Quick Journey to Using Ray: How We Implement Ray and Anyscale to Speed up our ML Processes (Juan Roberto Honorato & Domingo Ortuzar, Anastasia)
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68
Detection of Sensitive Personal Data in Large Data Sets Using Ray with Deep Learning (Srinivasa Rao Aravilli, Visa)
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220
Ray and Anyscale: An Optimization Journey (Nachi Mehta, OXW.io)
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110
Distributed XGBoost on Ray (Kai Fricke & Michael Mui)
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460
A 24x Speedup for Reinforcement Learning with RLlib + Ray (Raoul Khouri, Two Sigma)
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600
Ray Serve: Patterns of ML Models in Production (Simon Mo)
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320

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