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

End-to-End-LLMs-Workflows-with-Anyscale

Robert Nishihara

Goku Mohandas

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End to End LLMs Workflows with Anyscale

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Marwan Sarieddine

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Yunxuan Xiao

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AI Deep Dives with Anyscale

Anyscale - images

Scaling AI Workloads Efficiently and Reliably with Ray on EKS

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Build a chat assistant fast using Canopy from Pinecone and Anyscale Endpoints

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Building LLM Apps for Production, Part 1

Prefect Joint Webinar

Alex Streed

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Toby Rahloff

Phi Nguyen


Watch this webinar hosted by Anyscale, the company behind Ray, the unified framework for scalable computing, and Prefect, the modern data workflow and observability for your data science. Learn how Ray and Prefect combine to provide orchestration and observability for your data workflow at any scale easily, and hear a live case study from LiveEO, the satellite based infrastructure monitoring service, on how they use Prefect and Anyscale to streamline their workloads for their customers.

Learn about key aspects of ML scaling:
- __Bridge the gap between development and production__
Understand how to scale ML workloads from your laptop to the cloud with no code changes. With a single script prepare data, tune, train and scale your workloads.

- __Scale across multiple dimensions__
Hear how organizations are benefitting from embarrassingly parallel experiments and scaling across multiple cores, nodes, and data sources

- __Increase developer velocity and speed experimentation__
See how to speed model development and iterations without scaling complexity. Visualize, optimize, collaborate and standardize models and data pipelines.

- __Orchestration as a service__
Operationalize, automate and manage your workflows without having to worry about moving your data.

- __Coordination plane__
Increase visibility across all of your data and ML workflows.


Orchestrate Your Data Science Workflows at Scale

Arize Joint Webinar

Dat Ngo

dat-ngo

See how Ray and Arize combine to provide highly scalable and easily managed ML deployments, with automatic issue detection and quick troubleshooting. 

Watch this on-demand webinar hosted by Anyscale, the company behind Ray, the unified framework for scalable computing, and Arize, the leader in machine learning observability. See how Ray and Arize combine to provide ease of AI/ML development and observability along with the ability to understand performance, data quality and drift issues. 

Productionizing Machine Learning with Observability, Quality and Flexibility at Scale

W&B Joint Webinar

Anish Shah

Tricia Fu

Watch this on-demand webinar by Anyscale, the company behind Ray, the unified framework for scalable computing, and Weights & Biases, the leading developer-first MLOps platform. The webinar discusses how Ray and Weights & Biases are used together by developers and AI teams to ease AI/ML development, experimentation, experiment tracking, model scaling and model management. 


Simplify Building, Scaling, Tracking and Monitoring Your AI/ML Models

Use Cases Webinar (1)

Christy Bergman


Watch this on-demand webinar to see examples of how organizations successfully develop and deploy large-scale machine learning applications and address demand forecasting, NLP, scalable reinforcement learning, recommendation systems, estimated on-time arrival, dynamic pricing and more.

See examples of how organizations successfully develop and deploy large-scale machine learning applications using Ray, the Python-native unified distributed framework for AI/ML scaling:

- Scalable demand forecasting and the use of distributed time-series partitions
- Scalable reinforcement learning including use cases in gaming, trading, portfolio optimization, robotics, traffic optimization and more


How AI Teams Ease Scaling Popular AI/ML Use Cases

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Watch this on-demand webinar to learn how to make scaling ML and Python workloads a simple integrated experience. Watch the new Workspace Environment, powered by Ray, that makes it a simple developer experience to:
- Easily Scale any AI/ML and Python workloads without any complex infrastructure or scaling complexity
- Move and operationalize workloads to production without having to refactor any machine learning code
- Unify the deployment and scaling experience without having to stitch together many different AI/ML tools and frameworks 
- Scale any workload, even on large data, without consuming too much costly compute


Making ML Scaling a Simple Experience for Every Developer

Simplify Scaling Machine Learning

Antoni Baum

Watch this on-demand webinar to learn how to simply scale any AI or Python workload using Ray, the fastest growing unified compute framework for scaling machine learning workloads.

Learn how users are using Ray to build, manage and deploy scalable ML workloads.

See more highlights below: 

Simplify Scaling Machine Learning and Python Workloads

Webinar-Ray Serve 101

Paige Bailey

paige-headshot

Simon Mo

Edward Oakes

Join this webinar for a primer on Ray Serve - an easy-to-use, scalable model serving library built on top of Ray. 

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Ray Serve 101: Deploying your first ML model locally and as a managed service

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RL in production: A roundtable discussion with industry leaders

Webinar-RL roundtable

Richard Liaw

Adam Kelloway

Dr. Marc Weber

Marc Webber

Volkmar Sterzing

Ben Kasper

Reinforcement learning (RL) is the new kid on the machine learning block. Why are industry leaders giving RL a try? When aren’t traditional machine learning and deep learning techniques enough? 


Register

Webinar-XGBoost

In this webinar, we will cover how Ray, a universal distributed computing framework running on Anyscale, simplifies the end-to-end machine learning lifecycle and provides serverless compute without limits. We will go through an example from beginning to end using XGBoost.

Simplify and scale your XGBoost model using Ray on Anyscale

Webinar-Getting started with Anyscale

Charles Greer 

Javier Redondo

In this webinar, the Anyscale solutions team will guide you through the steps of taking your Python workloads to the cloud. Previous knowledge of Ray is helpful but not required as we demonstrate the concepts behind running distributed loads in the cloud, and how to interact with Ray and Anyscale in your Python code. 

Getting started with Anyscale concepts, code, and clusters

Webinar-Ray Datasets

Clark Zinzow

Alex Wu

Ray Datasets is a Ray-native distributed dataset library that serves as the standard way to load, process, and exchange data in Ray libraries and applications. It features performant distributed data loading, flexible parallel compute operations, and comprehensive datasource compatibility and distributed framework integrations, all behind an incredibly simple API.

Ray Datasets: Scalable data preprocessing for distributed ML

View Presentation

Will Drevo

Matthew Deng

Amog Kamsetty

Today, most frameworks for deep learning prototyping, training, and distributing to a cluster are either powerful and inflexible, or nimble and toy-like. Data scientists are forced to choose between a great developer experience and a production-ready framework. 

To fix this gap, the Ray ML team has developed Ray Train. 

Ray Train: Production-ready distributed deep learning

Watch the recording

Webinar-Intro to Ray Core

Jules Damji

This is an introductory and interactive session on concepts and code on Ray Core and Ray’s Ecosystem. The session covers why use and adopt Ray, touches upon Ray’s Ecosystem for scaling machine learning (ML) workloads, and provides an overview of Ray’s architecture and components.

We’ll also cover Ray Core APIs to write remote functions, actors and understand Ray’s basic design patterns for writing distributed Python applications. 

By the end of this session, you will learn:
- Why distributed computing is a necessity today
- What Ray native libraries to use to scale your ML workloads
- How to use Ray Core APIs


Introduction to Ray Core and its Ecosystem

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

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Intro to Anyscale: Production-Grade Distributed Computing with Ray

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Agentic AI in Practice: Build Scalable and Production Ready MCP Servers

Unlocking Agentic AI with MCP 

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