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Waleed Kadous

Jun Gong

Antoni Baum

Richard Liaw

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In part 4 of our Generative AI series, we share how to build a system for fine-tuning & serving LLMs in 40 minutes or less.

fine-tune-stack

How to Fine-Tune a 6 Billion Parameter LLM for Less Than $7

How to fine tune and serve LLMs simply, quickly and cost effectively using Ray + DeepSpeed + HuggingFace

Eric Liang

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Kai Fricke

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

layered-stack

How Ray Solves Generative AI & LLM Infrastructure Challenges

How Ray solves common production challenges for generative AI infrastructure

Jules S. Damji

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.

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

Many Models Batch Training at Scale with Ray Core

Chandler Gibbons

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Three ways to speed up XGBoost model training

Qingyun Wu

Chi Wang

Michael Galarnyk

FLAML XGBOOST Ray Tune

Fast AutoML with FLAML + Ray Tune

Will Drevo

Ray + LightGBM

Introducing Distributed LightGBM Training with Ray | Anyscale

Introducing Distributed LightGBM Training with Ray

Ray Summit 2024, the annual Ray community conference, is back in San Francisco, CA this September 30th through October 2nd. It is a two-day event with a third day dedicated to training.

Call for Proposals is open Blog Post

Ray Summit 2024 Call for Proposals is now open

Intel and Anyscale have integrated Ray with Gaudi3. Gaudi3 is AI-optimized for efficient training and other machine learning workloads.

Anyscale - Intel

Accelerating AI: Harnessing Intel(R) Gaudi(R) 3 with Ray 2.10

Anyscale is teaming with NVIDIA to combine the developer productivity of Ray Serve and RayLLM with the cutting-edge optimizations from NVIDIA Triton Inference Server software and the NVIDIA TensorRT-LLM library.

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Low-latency Generative AI Model Serving with Ray, NVIDIA Triton Inference Server, and NVIDIA TensorRT-LLM

Generating embeddings is a critical task for developing successful RAG applications, and Anyscalew with Pinecone is the most cost-efficient solution for this workflow.

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RAG at Scale: 10x Cheaper Embedding Computations with Anyscale and Pinecone

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