AI Infrastructure & MLOps

Omnisearch indexes audio, video, documents, and text so users can search across their website content.

Lucent brings the Lucid library approach to PyTorch, according to its repository description.

Hypernets offers a general automated machine learning framework for developing end to end AutoML toolkits in specific domains.

C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library.

This site explains machine learning operations, commonly shortened to MLOps.

Fiber presents a simple approach to distributed computing for AI workloads.

The Feast repository describes an open source feature store for AI and machine learning projects.

Unsloth provides a local interface for running and training language and diffusion models, including GGUF and MLX support.

MLX provides an array computing framework designed for Apple silicon devices.

Kubeflow provides a machine learning toolkit for deploying workflows on Kubernetes.

The Hydrosphere repository describes an MLOps platform for machine learning operations.

OpenAI compatible API for LLMs and embeddings (LLaMA, Vicuna, ChatGLM and many others).

Parris, the automated infrastructure setup tool for machine learning algorithms.

Supermemory provides memory and continual-learning tools for AI agents through an API, plugins, and an MCP interface.

Entroly reduces AI context size with reversible compression and receipts for recovering the original content.

Flama serves predictive and generative AI models as APIs, with compatible endpoints, a chat interface, and MCP support.

Upstage builds large language models and document-processing engines for workplace workflows.

Train and run models with OneFlow, a scalable deep learning framework for distributed machine learning workloads.

Keras provides a deep learning API for building and training machine learning models.

Apache Mahout develops Qumat, a backend agnostic quantum circuit library, and QDP, a GPU accelerated quantum data encoding engine.

Quix is the agentic AI platform for hardware engineering — turn test-rig and sensor data into real-time insight.

OptiMate is a collection of libraries for optimizing AI model performance; the repository is marked as a legacy project.

dstack coordinates AI compute and supports training and inference across GPU clouds, Kubernetes, VMs, and bare metal.

9Router routes AI requests across providers with automatic fallback and OpenAI-compatible endpoints.