Ray Summit brings together the community of architects, machine learning engineers, researchers, and other developers building the next generation of scalable, distributed, high-performance Python and machine learning applications. The conference will include those who are already using Ray platform and those who are interested in learning how Ray can meet their needs.

Erik E

Erik Erlandson

Erik Erlandson
Software Engineer, Red Hat, AI Center of Excellence

Powering Open Data Hub with Ray

sahika

Sahika Genc

Sahika Genc, Principal Applied Scientist, Amazon

Introducing Amazon SageMaker Kubeflow Reinforcement Learning Pipelines for Robotics

davit

Davit Buniatyan

Davit Buniatyan, Founding CEO, Activeloop

Software 2.0 Needs Data 2.0: A New Way of Storing and Managing Data for Efficient Deep Learning

chris q

Chris Qin

Chris Qin, Senior Software Engineer, Alibaba Cloud

Mars-on-Ray: Accelerating Large-scale Tensor and DataFrame Workloads

amog

Amog Kamsetty

Amog Kamsetty, Software Engineer, Anyscale

A Growing Ecosystem of Scalable ML Libraries on Ray

jeroen

Jeroen Bédorf

Ishaan Sood

Jeroen Bédorf (Senior System Architect) & Ishaan Sood (Neural Network and Software Engineer), minds.ai

Using RLLib in an Enterprise Scale Reinforcement Learning Solution

roger

Roger Ng, M.D.

Roger Ng, M.D., Cardiologist, Virginia Mason Heart Institute

The Journey from AI to Bedside: Translating Academic Research to Serving Patients Using Ray

alex

Alex Wu

A Deep Dive into Ray’s Scheduling Policy

stanley

Stanley Bishop

Stanley Bishop-Rosal

Stanley Bishop, Head of Data Science, Drift Biotechnologies

Machine Learning and Microbiology in the Cloud Through Ray

linsong

Linsong Chu

Linsong Chu, Research Engineer, IBM Research

Anomaly Detection on Remote Sensing with Ray + Horovod

From the creators of Ray, Anyscale is a framework for building machine learning applications at any scale originating from the UC Berkeley RISELab.

Ray Summit

Ray Summit

Powering Open Data Hub with Ray

Ray Summit

Introducing Amazon SageMaker Kubeflow Reinforcement Learning Pipelines for Robotics

Ray Summit

Software 2.0 Needs Data 2.0: A New Way of Storing and Managing Data for Efficient Deep Learning

Ray Summit

Mars-on-Ray: Accelerating Large-scale Tensor and DataFrame Workloads

Ray Summit

A Growing Ecosystem of Scalable ML Libraries on Ray

Ray Summit

Using RLLib in an Enterprise Scale Reinforcement Learning Solution

Ray Summit

The Journey from AI to Bedside: Translating Academic Research to Serving Patients Using Ray

Ray Summit

A Deep Dive into Ray’s Scheduling Policy

Ray Summit

Machine Learning and Microbiology in the Cloud Through Ray

Ray Summit

Anomaly Detection on Remote Sensing with Ray + Horovod

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