Ray RLlib

Ray Train

Ray Datasets

RayDP

Retail

Gaming

Government

Financial

Ray Serve

Anyscale

Ray Core

Healthcare

Technology

Here you can find where the Anyscale and Ray teams will be in attendance at industry events across the globe. 

Industry Event

Press Interview

On-Demand Webinar

Webinar

RL Summit

Weekly Demo

Ray Meetup

Anyscale GA Launch

Anyscale Academy provides training on the Ray ecosystem, tailored for data scientists, data engineers, and MLOps engineers.

Anyscale Academy Logo

Anyscale Academy

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.

Ray Summit

edward j

Edward Junprung

Sahar Esmaeilzadeh

Edward Junprung (Customer Success) & Sahar Esmaeilzadeh (Lead Simulation Engineer), Pathmind

Applying Ray and RLlib to Real-life Industrial Use Cases

wei chen

Wei Chen

Wei Chen, Deep Learning Software Engineer, NVIDIA

Large Scale Data Loading and Data Preprocessing with Ray

raghu

Raghu Ganti

Raghu Ganti, Principal Research Staff Member, IBM

Scaling and Unifying SciKit Learn and Spark Pipelines using Ray

peter

Peter Noseworthy, M.D.

Peter Noseworthy

Peter Noseworthy, M.D., Professor, Mayo Clinic College of Medicine | Cardiologist (Cardiac Electrophysiology) Director, Heart Rhythm and Physiologic Monitoring Laboratory, Mayo Clinic

How Smart is the ECG? Lessons from Advanced ECG Analytics

hao z

Hao Zhang

Collective-on-Ray: High-performance Collective Communication for Distributed Machine Learning on Ray

melih

Melih Elibol

Melih Elibol, Graduate Researcher, UC Berkeley

NumS: Scalable Array Programming for the Cloud

tanja

Tanja Bayer

Tanja Bayer, Machine Learning Engineer, Widas Technologie Services GmbH

Leveraging the Possibilities of Ray Serve in Implementing a Scalable, Fully Automated Digital Authentication Service

Nic Hohn thumb

 Nicolas Hohn

A deeper dive into Nic Hohn's keThe increasing prevalence of high-fidelity industrial digital twins is providing a range of opportunities to apply Reinforcement Learning (RL) techniques outside of traditional academic examples and video games. We will present how McKinsey supported Emirates Team New Zealand in winning the 36th America’s Cup by leveraging some of the latest AI techniques and technology platforms. The America’s Cup is the oldest sports trophy in the world, but is first and foremost a technology competition. It’s a race against time to design, test, and deliver the fastest yacht on race day, with a team of Olympic-level athletes ready to sail it and make it fly thanks to cutting-edge hydrofoils. We will describe how we used deep Reinforcement Learning to speed up their design process. In particular, we will deep dive on the underlying technologies we used, from scaling RL training on a Ray cluster, to using Tune to manage results variability.ynote, "Making Boats Fly with AI on Ray". 

Deeper Dive on Making Boats Fly with AI on Ray

charles

Dr. Charles He

Dr. Charles He, Chief Architect of Computing and Storage, Ant Group

Fusion Computing at Ant Group

marvin

Marvin Theimer

Marvin Theimer, Distinguished Engineer, Amazon Web Services

Cloud Computing Made Better: An AWS View on Serverless Computing

matt j

Matt Johnson

Matt Johnson, Research Scientist, Google Brain | Tech Lead, JAX

JAX: Accelerated Machine Learning Research via Composable Function Transformations in Python

dawn

Dawn Song

Dawn Song 2

Towards Building a Responsible Data Economy

tianqi

Tianqi Chen

Tianqi Chen, Assistant Professor, ML & CS Department, Carnegie Mellon University | CTO, OctoML | Creator of Apache TVM, XGBoost, and the co-creator of Apache MXNet

Elements of Learning Systems

eric b

Eric Brewer

Eric Brewer, VP Infrastructure & Google Fellow, Google | Professor, UC Berkeley

Lessons from the Kubernetes Adventure

panel

Zhe Zhang

Xu Ning, PhD

Xu Ning

Azeem Ahmed

Bhavya Agarwal

Zhe Zhang (Open Source Engineering Lead), Anyscale; Xu Ning, PhD, (Senior Engineering Manager), Uber; Azeem Ahmed (Director of Data Platform Engineering), Shopify; Bhavya Agarwal (ML Engineer), Robinhood

Panel: ML Platform on Ray

dong li

Dong Li

Dong Li, PMC Member, Apache Kylin | Founding Member & Product Head, Kyligence

Simplify Big Data & AI on Spark and Ray with MLSQL

k & l

Kai Huang

Luyang Wang

Luyang Wang 2

Kai Huang, Software Engineer, Intel & Luyang Wang, Director of Machine Learning Engineering & Data Science, Restaurant Brands International

Mobile Order Click-Through Rate (CTR) Recommendation with Ray on Apache Spark at Burger King

stefan

 Stefan Schneider

Stefan Schneider, PhD Candidate & Researcher, Paderborn University

DeepCoMP: Multi-agent Reinforcement Learning for Multi-cell Selection in 5G and Beyond

A one stop shop for all events, past and future, to connect and learn from the global community of Ray experts.

hero2

Thumbnail: Webinar: Getting Started with Distributed Training at Scale

Getting Started with Distributed Training at Scale

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 Live Virtual Hands On Lab: Building Scalable AI with Ray 

Lineage Tracking webinar cover photo

From Unity Catalog to MLflow: Experiment Tracking and Lineage for Ray

Virtual Hands on Lab

 Live Virtual Hands On Lab: Building Scalable AI with Ray

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Event Types

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Events