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Decks

Using Reinforcement Learning to Optimize IAP Offer Recommendations in Mobile Games (Emiliano Castro, Vinicius Alves, & Felipe Antunes, Wildlife Studios)

Scaling Interactive Data Science with Modin and Ray (Devin Petersohn, UC Berkeley))

Per-epoch Shuffling Data Loader: Mix It Up As You Train! (Clark Zinzow, Anyscale)

W&B Tables and Ray (Chris Van Pelt, Weights & Biases)

Data Processing on Ray (SangBin Cho, Anyscale)

Detection of Sensitive Personal Data in Large Data Sets Using Ray with Deep Learning (Srinivasa Rao Aravilli, Visa)

The Quick Journey to Using Ray: How We Implement Ray and Anyscale to Speed up our ML Processes (Juan Roberto Honorato & Domingo Ortuzar, Anastasia)

Petabyte Scale Datalake Table Management with Ray, Arrow, Parquet, and S3 (Patrick Ames, Amazon)

State of Ray (Robert Nishihara, Anyscale)

Making Distributed Computing Easy (Ion Stoica, Anyscale)

Anyscale Demo: Machine Learning Application from Dev to Prod (Edward Oakes, Anyscale)

End-to-End AutoML with Ludwig on Ray (Travis Addair)

Offline Reinforcement Learning with RLlib (Edi Palencia)

Ray Serve: Patterns of ML Models in Production (Simon Mo)

A 24x Speedup for Reinforcement Learning with RLlib + Ray (Raoul Khouri, Two Sigma)

Distributed XGBoost on Ray (Kai Fricke & Michael Mui)

Ray and Anyscale: An Optimization Journey (Nachi Mehta, OXW.io)

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