Bridge the gap between deep learning training and serving
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Updated
Jul 3, 2024 - C++
Bridge the gap between deep learning training and serving
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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.
Serve, optimize and scale PyTorch models in production
A universal scalable machine learning model deployment solution
AI + Data, online. https://vespa.ai
A high-performance inference system for large language models, designed for production environments.
A scalable inference server for models optimized with OpenVINO™
A flexible, high-performance serving system for machine learning models
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
Lineage metadata API, artifacts streams, sandbox, API, and spaces for Polyaxon
Friendli: the fastest serving engine for generative AI
This repository provides AI/ML service(MachineLearning model serving) modernization solution using Amazon SageMaker, AWS CDK, and AWS Serverless services.
ClearML - Model-Serving Orchestration and Repository Solution
RayLLM - LLMs on Ray
Docs for torchpipe: https://github.com/torchpipe/torchpipe
In this repository, I will share some useful notes and references about deploying deep learning-based models in production.
Database system for AI-powered apps
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