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Anyscale

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Decks

Fast and efficient hyperparameter tuning with Ray Tune
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1.3k
Building a scalable ML model serving API with Ray Serve
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1.2k
[Webinar] An introduction to Ray for scaling machine learning (ML) workloads
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450
Fusion Computing at Ant Group (Dr. Charles He, Ant Group)
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310
Scaling and Unifying SciKit Learn and Spark Pipelines using Ray (Raghu Ganti, IBM)
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290
Introducing Amazon SageMaker Kubeflow Reinforcement Learning Pipelines for Robotics (Sahika Genc, Amazon)
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520
Software 2.0 Needs Data 2.0: A New Way of Storing and Managing Data for Efficient Deep Learning (Davit Buniatyan, Activeloop)
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190
Using RLLib in an Enterprise Scale Reinforcement Learning Solution (Jeroen Bédorf & Ishaan Sood, minds.ai)
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260
Powering Open Data Hub with Ray (Erik Erlandson, Red Hat, AI Center of Excellence)
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380
A Growing Ecosystem of Scalable ML Libraries on Ray (Amog Kamsetty, Anyscale)
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150
Machine Learning and Microbiology in the Cloud Through Ray (Stanley Bishop, Drift Biotechnologies)
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180
Mars-on-Ray: Accelerating Large-scale Tensor and DataFrame Workloads (Chris Qin, Alibaba Cloud)
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270
Collective-on-Ray: High-performance Collective Communication for Distributed Machine Learning on Ray (Hao Zhang, UC Berkeley)
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230
Applying Ray and RLlib to Real-life Industrial Use Cases (Edward Junprung & Sahar Esmaeilzadeh, Pathmind)
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580
Large Scale Data Loading and Data Preprocessing with Ray (Wei Chen, NVIDIA)
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390
A Deep Dive into Ray’s Scheduling Policy (Alex Wu, Anyscale)
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400
Anomaly Detection on Remote Sensing with Ray + Horovod (Linsong Chu, IBM Research)
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220
NumS: Scalable Array Programming for the Cloud (Melih Elibol, UC Berkeley)
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150

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