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Decks

Fast and efficient hyperparameter tuning with Ray Tune

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1.4k

Building a scalable ML model serving API with Ray Serve

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1.3k

[Webinar] An introduction to Ray for scaling machine learning (ML) workloads

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550

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

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370

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

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380

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

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610

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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280

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

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340

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

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490

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

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220

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

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240

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

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370

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

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330

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

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650

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

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480

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

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580

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

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280

NumS: Scalable Array Programming for the Cloud (Melih Elibol, UC Berkeley)

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270

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