AI Infrastructure & MLOps

Helps teams build, deploy, observe or operate machine-learning systems.

Metorial supports deploying, serving, monitoring, or operating AI and machine-learning systems.

Milvus is open source vector database for production AI, written in Go and C++, scalable and blazing fast for billions of embedding vectors.

Full-stack AI platform focused on multimodal agents and consumer-scale deployment.

Version and deploy your ML models following GitOps principles.

An open-source platform for tracking ML experiments, evaluating models and prompts, deploying models, and adding LLM observability. opensource.

MLOps Community supports deploying, serving, monitoring, or operating AI and machine-learning systems.

MLOps Guide supports deploying, serving, monitoring, or operating AI and machine-learning systems.

MLOps Now supports deploying, serving, monitoring, or operating AI and machine-learning systems.

Open source MLOps platform that helps you collaborate, reproduce and share your ML work.

Machine learning model serving framework with dynamic batching and pipelined stages, provides an easy-to-use Python interface.

Private AI for individuals, teams, and organizations. Workspaces, agents, models, and knowledge—one connected system on your terms.

MyVibe supports deploying, serving, monitoring, or operating AI and machine-learning systems.

Proof-of-concept OpenAI Gym environment for Neural Architecture Search (NAS).

Ncnn is a high-performance neural network inference framework optimized for the mobile platform.

Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.

Easy-to-use library to boost AI inference.

A framework providing the right abstractions to ease research, development, and deployment of your ML pipelines.

Nimblebox supports deploying, serving, monitoring, or operating AI and machine-learning systems.

An Easy-to-Use and High-Performance AI deployment framework.

Neural networks framework in pure C: training and inference, no dependencies.

Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.

Simplifies and accelerates MLOps by bridging the gap between ML models and edge hardware.

Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.