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

AI + Data, online. https://vespa.ai

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

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

Serve, optimize and scale PyTorch models in production

⚡️An Easy-to-use and Fast Deep Learning Model Deployment Toolkit for ☁️Cloud 📱Mobile and 📹Edge. Including Image, Video, Text and Audio 20+ main stream scenarios and 150+ SOTA models with end-to-end optimization, multi-platform and multi-framework support.

Database system for AI-powered apps

Lightning-fast serving engine for any AI model of any size. Flexible. Easy. Enterprise-scale.

TensorFlow template application for deep learning

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.

DELTA is a deep learning based natural language and speech processing platform.

RayLLM - LLMs on Ray

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

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

A scalable inference server for models optimized with OpenVINO™

A unified end-to-end machine intelligence platform

Python + Inference - Model Deployment library in Python. Simplest model inference server ever.

Lineage metadata API, artifacts streams, sandbox, API, and spaces for Polyaxon