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Learn the infrastructure challenges for supporting workloads production deployments around foundation models, and how Ray tackles these challenges.

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How Ray Solves Generative AI & LLM Infrastructure Challenges

How Ray solves common production challenges for generative AI infrastructure

Cade Daniel

Jules S. Damji

Zhe Zhang

The Ray 2.3 release features exciting improvements across the Ray ecosystem. In this blog post, we will highlight new features, performance enhancements, and support for new platforms. 

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Announcing Ray 2.3: performance improvements, new features and new platforms

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.

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Ray 2.2 | Improved Experience, Performance & Stability

Ray 2.2: Improved developer experience, performance and stability

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

Paige Bailey

Jian Xiao

Chandler Gibbons

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Ray 1.12: Ray AI Runtime (alpha), usage data collection, and more

Juan Navas

Learn how to use the Ray Tune library to distribute hyperparameter tuning tasks among several computers.

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How to Distribute Hyperparameter Tuning Using Ray Tune

How to distribute hyperparameter tuning using Ray Tune

Explore a practical hands-on case showing how to tune hyperparameters on XGBoost. Learn how to easily deploy and optimize ML models.

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Guide to XGBoost Hyperparameter Tuning

How to tune hyperparameters on XGBoost

Matthew Deng

Amog Kamsetty

Will Drevo

Distributed Deep Learning with Ray Train is Now In Beta

Distributed deep learning with Ray Train is now in Beta

Qingyun Wu

Chi Wang

Michael Galarnyk

FLAML XGBOOST Ray Tune

Fast AutoML with FLAML + Ray Tune

Ray Lightning

Introducing Ray Lightning: Multi-node PyTorch Lightning training made easy

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Introducing Distributed XGBoost Training with Ray

Hao Zhang

Sender Receiver (Introducing Collective Communication Primitive APIs in Ray)

Introducing Collective Communication Primitive APIs in Ray

Robert Nishihara

PyTorch + Ray

Getting Started with Distributed Machine Learning with PyTorch and Ray

Bill Chambers

Jieru Hu

Hydra+Ray (Anyscale)

Configuring and Scaling ML with Hydra + Ray

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Hyperparameter Search with Hugging Face Transformers

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Google Cloud Integrates Anyscale's RayTurbo with GKE

Simplifying AI Development at Scale: Google Cloud Integrates Anyscale's RayTurbo with GKE

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

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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.

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Anyscale Available on AWS Marketplace

Anyscale Now Available on AWS Marketplace and Achieves Generative AI Competency

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Don't miss out! Join us for the Ray + AI Infra Summit in NYC on May 20.

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Posts by Richard Liaw