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Decks

Scaling and Unifying SciKit Learn and Spark Pipelines using Ray (Raghu Ganti, IBM)

Fusion Computing at Ant Group (Dr. Charles He, Ant Group)

Anomaly Detection on Remote Sensing with Ray + Horovod (Linsong Chu, IBM Research)

A Deep Dive into Ray’s Scheduling Policy (Alex Wu, Anyscale)

Large Scale Data Loading and Data Preprocessing with Ray (Wei Chen, NVIDIA)

Applying Ray and RLlib to Real-life Industrial Use Cases (Edward Junprung & Sahar Esmaeilzadeh, Pathmind)

Collective-on-Ray: High-performance Collective Communication for Distributed Machine Learning on Ray (Hao Zhang, UC Berkeley)

Mars-on-Ray: Accelerating Large-scale Tensor and DataFrame Workloads (Chris Qin, Alibaba Cloud)

Machine Learning and Microbiology in the Cloud Through Ray (Stanley Bishop, Drift Biotechnologies)

A Growing Ecosystem of Scalable ML Libraries on Ray (Amog Kamsetty, Anyscale)

Powering Open Data Hub with Ray (Erik Erlandson, Red Hat, AI Center of Excellence)

Using RLLib in an Enterprise Scale Reinforcement Learning Solution (Jeroen Bédorf & Ishaan Sood, minds.ai)

Software 2.0 Needs Data 2.0: A New Way of Storing and Managing Data for Efficient Deep Learning (Davit Buniatyan, Activeloop)

Introducing Amazon SageMaker Kubeflow Reinforcement Learning Pipelines for Robotics (Sahika Genc, Amazon)

Simplify Big Data & AI on Spark and Ray with MLSQL (Dong Li, Kyligence)

Application of Online Resource Allocation Based on Ray (Fang Fengbin, Ant Group)

Towards Building a Responsible Data Economy (Dawn Song, UC Berkeley)

JAX: Accelerated Machine Learning Research via Composable Function Transformations in Python (Matt Johnson, Google Brain)

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