Search results

Curated list of awesome vector search framework/engine, library, cloud service and research papers to vector similarity search.

MII makes low-latency and high-throughput inference possible, powered by DeepSpeed.

10x faster, cheaper, and better vector database.

Eurybia monitors data and model drift over time and securizes model deployment with data validation.

A library for efficient similarity search and clustering of dense vectors.

FeatherCNN is a high performance inference engine for convolutional neural networks.

An enterprise-grade, high performance feature store.

A Virtual Feature Store. Turn your existing data infrastructure into a feature store.

Open-source self-hostable end-to-end LLMOps platform unifying tracing, evals, simulations, datasets, gateway, and guardrails.

Production-grade SDK for observability, automated evaluations and prompt management with sub-100ms guardrails for LLM/agent workflows.

Go binding for MXNet c predict api to do inference with a pre-trained model.

Create customizable UI components around your models.

Build multimodal AI services via cloud native technologies · Model Serving · Generative AI · Neural Search · Cloud Native.

Kubernetes-based system for hyperparameter tuning and neural architecture search.

Kubernetes custom resource definition for serving ML models on arbitrary frameworks.

Open source MLOps project that eases model handoffs between data scientist and DevOps.

Standardized Serverless ML Inference Platform on Kubernetes.

Open-source all-in-one platform for engineering AI products. Traces, Evals, Datasets, Labels.

Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!

Neural network inference from the command line, implemented in CHICKEN Scheme.

A lightweight, portable pure C99 onnx inference engine for embedded devices with hardware acceleration support.

Version and deploy your ML models following GitOps principles.

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.