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

Watch on-demand

Ray logo (webinar thumbnail)

Jules Damji

Antoni Baum

Modern machine learning (ML) workloads, such as deep learning and large-scale model training, are compute-intensive and require distributed execution. Ray was created in the UC Berkeley RISELab to make it easy for every engineer to scale their applications and ML workloads, without requiring any distributed systems expertise, making distributed programming easy. 

Join Jules S. Damji, developer advocate at Anyscale, and Antoni Baum, software engineer at Anyscale, for an introduction to Ray for scaling your ML workloads. Learn how Ray libraries (eg., Ray Tune, Ray Serve, etc) help you easily scale every step of your ML pipeline — from model training and hyperparameter search to inference and production serving.


Scaling Machine Learning Workloads with Ray

Tune

Will Drevo

Hyperparameter tuning or optimization is used to find the best performing machine learning (ML) model by exploring and optimizing the model hyperparameters (eg. learning rate, tree depth, etc). It is a compute-intensive problem that lends itself well to distributed execution. 

Ray Tune is a Python library, built on Ray, that allows you to easily run distributed hyperparameter tuning at scale. Ray Tune is framework-agnostic and supports all the popular training frameworks including PyTorch, TensorFlow, XGBoost, LightGBM, and Keras. 

Join this webinar with Will Drevo, product manager for Ray machine learning libraries, for an overview of Ray Tune and demo of using it for tuning a deep learning model. 


Fast and efficient hyperparameter tuning with Ray Tune

ray serve

Tricia Fu

Ray Serve is a framework-agnostic and Python-first model serving library built on Ray. In this introductory webinar on Ray Serve, we will highlight how Ray Serve makes it easy to deploy, operate and scale a machine learning API. 

The core of the webinar will be a live demo that shows how to build a scalable API using Natural Language Processing models. 


Building a scalable ML model serving API with Ray Serve 

survey thumb

Robert Nishihara

Bill Chambers

Modern machine learning (ML) workloads, such as deep learning and large-scale model training, are compute-intensive and require distributed execution. Ray was created in the UC Berkeley RISELab to make it easy for every engineer to scale their applications, without requiring any distributed systems expertise. 

Join Robert Nishihara, co-creator of Ray, and Bill Chambers, product lead for Ray, for an introduction to Ray for scaling your ML workloads. Learn how Ray libraries (eg. Ray Tune, Ray Serve, etc) help you easily scale every step of your ML pipeline — from model training and hyperparameter search to production serving. 

__[View Slides >>](https://speakerdeck.com/anyscale/webinar-an-introduction-to-ray-for-scaling-machine-learning-ml-workloads)__

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

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

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

hero2

ray compiled graphs

[Ray Meetup] Ray Compiled Graphs Deep Dive

Supercharging AI Platforms with Anyscale

Optimized AI Workloads on Anyscale with RayTurbo

Call for Proposals is open Blog Post

Ray Summit 2024

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