serving

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

A flexible, high-performance serving system for machine learning models

A Cloud Native Batch System (Project under CNCF)

An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models

learn LLM inference system on Apple Silicon for systems engineers: build a tiny vLLM + Qwen

In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

A minimal Python framework for building custom AI inference servers with full control over logic, batching, and scaling.

High-performance Inference and Deployment Toolkit for LLMs and VLMs based on PaddlePaddle

A comprehensive guide to building RAG-based LLM applications for production.

A multi-modal vector database that supports upserts and vector queries using unified SQL (MySQL-Compatible) on structured and unstructured data, while meeting the requirements of high concurrency and ultra-low latency.

A flexible, high-performance carrier for machine learning models（『飞桨』服务化部署框架）

A scalable inference server for models optimized with OpenVINO™

Generic and easy-to-use serving service for machine learning models

A unified end-to-end machine intelligence platform