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Hao Chen

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

Stephanie Wang

Eric Liang

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Data loading & preprocessing can easily become the performance bottleneck in ML pipelines. With Ray Data, data loading can be fast, flexible and scalable.

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Fast, Flexible and Scalable Data Loading for ML Training

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

Amog Kamsetty

Liguang Xie

ByteDance, the company behind Tiktok, leverages multi-modal models to enable many applications, such as text-based image retrieval or object detection.

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How ByteDance Scales Offline Inference with Multi-Modal LLMs

How ByteDance Scales Offline Inference with multi-modal LLMs to 200 TB Data

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.

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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

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Posts by Hao Chen

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