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Stephanie Wang

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Eric Liang

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Fast, flexible, and scalable data loading for ML training with Ray Data

Cade Daniel

Chen Shen

Richard Liaw

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How continuous batching enables 23x throughput in LLM inference while reducing p50 latency

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Streaming distributed execution across CPUs and GPUs

Amog Kamsetty

Jules S. Damji

Figure 1

Offline Batch Inference: Comparing Ray, Apache Spark, and SageMaker

Antoni Baum

Jun Gong

Kai Fricke

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How Ray solves common production challenges for generative AI infrastructure

Robert Nishihara

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Training One Million Machine Learning Models in Record Time with Ray

Zhe Zhang

3 ways of batch inference in Ray.

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

Clark Zinzow

Alex Wu

Jiajun Yao

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Ray Datasets for large-scale machine learning ingest and scoring

Waleed Kadous

3rdGenTasks andActors

Deep Dive: Data Ingest in a Third Generation ML Architecture

Ray Distributed Library Patterns (Figure 2)

Ray Distributed Library Patterns

Sang Cho

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Data Processing Support in Ray

Ben Lorica

Ion Stoica

Birds

The Ideal Foundation for a General Purpose Serverless Platform

Anyscale on Azure Enters Public Preview- Build and Deploy AI at Scale Inside Your Own Azure Tenant

Anyscale on Azure Enters Public Preview: Build and Deploy AI at Scale Inside Your Own Azure Tenant

Reimagining ML Operations with Agent Skills- a New Maturity Model for On-Call

Reimagining ML Operations with Agent Skills: a new maturity model for on-call

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Introducing the Anyscale Agent Skill for LLM Post-Training

Announcing first-party service - Anyscale on Azure

Announcing Anyscale on Azure: Build, Run and Scale AI-Native Workloads Securely on Azure Infrastructure

Ray joins PyTorch Foundation

Ray is Joining The PyTorch Foundation

20x Faster Training Data Reads with Alluxio and Ray Data- A Cross-Region Benchmark

20x Faster Training Data Reads with Alluxio and Ray Data: A Cross-Region Benchmark

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Posts by Eric Liang