Ray RLlib

Ray Train

Ray Datasets

RayDP

Retail

Gaming

Government

Financial

Ray Serve

Anyscale

Ray Core

Healthcare

Technology

Ray Roadshow

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

Watch tutorial

rllib 

Sven Mika

A beginner’s tutorial for working with multi-agent environments, models, and algorithms.

“Hands-on RL with Ray’s RLlib” is a beginners tutorial for working with reinforcement learning (RL) multi-agent environments, models, and algorithms using Ray’s RLlib library. RLlib offers high scalability, a large list of algorithms to choose from (offline, model-based, model-free, etc..), support for TensorFlow and PyTorch, and a unified API for a variety of applications and customizations. This tutorial includes a brief introduction to provide an overview of concepts (e.g. why RL) before proceeding to RLlib models, hyperparameter tuning, debugging, student exercises, Q/A, and more. All code will be provided as .py files in a GitHub repo.


Hands-on Reinforcement Learning with Ray’s RLlib

ray core

Paco Nathan

A Guided Tour of Ray Core covers an introductory, hands-on coding tour through the core features of Ray, which provide powerful yet easy-to-use design patterns for implementing distributed systems in Python. This training includes a brief talk to provide overview of concepts, then coding for remote functions, actors, parallel iterators, and so on. Then we'll follow with Q&A. All code is available in notebooks in the GitHub repo. 


Ray Core Tutorial

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

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

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

hero2

rayday seattle

Ray Day: Seattle

Live Virtual Hands On Lab: Distributed Training at Scale with Ray and PyTorch

From Prototype to Production: Securely Accelerating Physical AI with Vision-Language-Action (VLAs) Models

Promotional webinar graphic from Anyscale on a black background. Text reads ‘Scaling LLM Fine-Tuning with FSDP, DeepSpeed, and Ray.’ The date and times are shown as Feb 19, 8:30 AM PT, 11:30 AM ET, and 5:30 PM CET. The Anyscale logo appears at the top left with the word ‘Webinar’ at the top right. At the bottom left is a headshot of Suman Debnath, labeled Principal Developer Advocate at Anyscale.

Webinar: Scaling LLM Fine-Tuning with FSDP, DeepSpeed, and Ray

Scaling LLM Fine-Tuning with FSDP, DeepSpeed, and Ray

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

This section is used to order the "Types" and "Tags" that show up for filters on the Event Index

Event Types

All Products / Libraries

Join us in-person at Ray Day: Seattle on March 31 - 

Powered by Ray, Anyscale empowers AI builders to run and scale all ML and AI workloads on any cloud and on-prem.

Learn why Anyscale is best place to run Ray

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Events

Anyscale Academy

Hands-on Reinforcement Learning with Ray’s RLlib

Anyscale Academy

Ray Core Tutorial

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

Ray Summit

Applying Ray and RLlib to Real-life Industrial Use Cases

Ray Summit

Large Scale Data Loading and Data Preprocessing with Ray

Ray Summit

Scaling and Unifying SciKit Learn and Spark Pipelines using Ray

Ray Summit

How Smart is the ECG? Lessons from Advanced ECG Analytics

Ray Summit

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

Ray Summit

NumS: Scalable Array Programming for the Cloud