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Richard Liaw

Richard Liaw headshot

The Ray 2.2 release features improved observability, better performance for data-intensive AI applications, increased stability, and better UX for RLlib.

View the stack trace or CPU flame graph of a Ray worker process from the dashboard.

Dashboard Flame Graph

Ray 2.2 | Improved Experience, Performance & Stability

Ray 2.2: Improved developer experience, performance and stability

Jules S. Damji

Antoni Baum

Learn how to conduct batch training using only Ray Core and stateless Ray tasks. We take an in-depth look at two approaches to employ Ray tasks to scale.

NYC taxi dirver data sete

How to Conduct Many-Model Batch Training at Scale with Ray

Many Models Batch Training at Scale with Ray Core

CfP Main page

Ray Summit 2023 | Share Your Ray Story

Ray Summit 2023 Call for Proposals is now open

Antoine Galataud

Phi Nguyen

Inouk Bourgon

Adrien Lafond

foobot_architecture

Foobot optimizes building energy efficiency by training fully autonomous control agents for HVAC systems, bringing energy savings to office buildings, hospitals, schools and commercial buildings.

Eric Liang

Eric Liang headshot

Robert Nishihara

More companies are needing to train & deploy many small ML models, often hundreds or thousands. Learn how Ray and Anyscale make that possible.

Many_model_image

Training 1 Million ML Models in Record Time | Anyscale

Training One Million Machine Learning Models in Record Time with Ray

Learn the 3 key features that will be critical to ensuring that your ML platform is well-positioned for modern AI applications.

Figure3-Shopify-Merlin-platform

Top 3 Key Elements of a Scalable ML Platform | Anyscale

Three Key Elements of a Scalable ML Platform 

Tengwei Cai, Yang Liu, Chengxi Luo, Xiaofeng Yang, Simon Mo

Calling Python Actor from Java Deployment

calling-pyton-deployment

Flexible, cross-language, distributed model inference framework: Ray Serve with Java API

Tengwei Cai, Yang Liu, Chengxi Luo, Xiaofeng Yang

ray-serving-adapt-ray-serve

How Ant Group uses Ray to build a Large-Scale Online Serverless Platform

 Abhishek Malvankar (IBM Research) and Dmitri Gekhtman (Anyscale)

Unblock your ML workload using KubeRay with the Multi-Cluster-App-Dispatcher (MCAD) Kubernetes controller.

mcad_ray_diagram

Gang Scheduling Ray Clusters on Kubernetes with MCAD

Gang Scheduling Ray Clusters on Kubernetes with Multi-Cluster-App-Dispatcher (MCAD)

Jake Carter

BlogBiolexis on Anyscale (4)

Biolexis Boosts Their New AI/ML Drug Discovery Platform Using Anyscale’s Fully-Managed Ray Platform

Houssem Eddine Gharbi

Tim Kreienkamp

Blog Adtriba on Anyscale (4)

Adtriba Accelerates and Advances Media Mix Modeling Using the Anyscale Fully-Managed Ray Platform

Richard Decal

Dendra Systems need speed & efficiency at an unprecedented scale. Learn how Ray & Anyscale have helped execute on their ML platform vision.

Blog Dendra on Anyscale (2)

How Massive-Scale ML is Made Easy With Ray & Anyscale

How Ray and Anyscale Make it Easy to Do Massive-Scale Machine Learning on Aerial Imagery 

SangBin Cho, Alan Guo, Ricky Xu, Eric Liang

Explore how you can view native time series metrics to garner insights into the scheduling and performance of your Ray workloads.

ray_integration_with_prometheus_and_graphana

Monitoring & Debugging Ray Workloads: Ray Metrics | Anyscale

Monitoring and Debugging Ray workloads: Ray Metrics

Zhe Zhang

This blog covers three methods of batch inference (distributed model evaluation) in Ray: from low-level using Ray Actors, to high-level using Ray Data.

We'll see how low-level Ray APIs allow you to control exactly "How" Ray is executing computations, while Ray's libraries enable you to specify just the "what" --- for a more out-of-the-box scalable experience.

3 ways of batch inference in Ray.

Model Batch Inference in Ray: Actors, ActorPool & Datasets

Model Batch Inference in Ray: Actors, ActorPool, and Datasets

Ion Stoica

Ion Stoica headshot

Ben Lorica

Ray For Success

Four Reasons Why Leading Companies Are Betting On Ray

Anyscale logo (webinar thumbnail)

Ray Summit, the Industry Event for Scalable AI, Unveils New Innovations in Ray and the Anyscale Platform; Features Speakers from Uber, IBM, OpenAI, Shopify, Dow and Numerous Global Organizations

Anyscale Unveils Ray 2.0 and Anyscale Innovations at Ray Summit 2022; Adds an Additional $99M Funding from Existing Investors Addition, Intel Capital, and Foundation Capital

Anyscale Ray Team

Ray 2.0

Announcing Ray 2.0

Deploy DeepSeek R1 on Anyscale for full control, seamless scalability, and optimized performance across any cloud or accelerator — without vendor lock-in.

Anyscale + DeepSeek

Deploying DeepSeek R1 on Anyscale

At Ray Summit 2024, we brought together AI experts and practitioners to dive into the future of distributed AI and scalable machine learning.

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Ray Summit 2024: Breaking Through the AI Complexity Wall

Explore the new and improved Anyscale Workspaces. Get advanced observability features so you spend less time on busywork and more time building AI apps.

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Explore Anyscale’s New UX

Anyscale’s New User Experience: A Comprehensive Overview

With Anyscale on AWS Marketplace, you can build, scale, and optimize AI/ML and Generative AI—all within the trusted AWS cloud environment.

Group 491 (1)

Anyscale Available on AWS Marketplace

Anyscale Now Available on AWS Marketplace and Achieves Generative AI Competency

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Anyscale is the leading AI application platform. With Anyscale, developers can build, run and scale AI applications instantly.

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