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Helps teams build, deploy, observe or operate machine-learning systems.
ANY.RUN sandbox for interactive malware analysis, behavior monitoring, and file investigation.
Janicek is an RSS or feed tool for discovering, reading, filtering, or managing web updates.
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.
Respan unifies LLM observability, evals, prompt optimization, and an AI gateway so teams can ship reliable AI applications.
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.
Kubeflow makes deployment of ML Workflows on Kubernetes straightforward and automated.
Public fire camera map for monitoring wildfire conditions and regional camera feeds.
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.
Algorithms for learning and inference with discrete probabilistic models.
A model gateway and unified interface for multiple model providers.
Observability and prompt management platform for LLM-based apps. Take your LLMs to the next level.
Ship AI agents that reason, remember, and act in TypeScript. Mastra provides memory, tools, MCP, and observability to go from prototype to production.
Helps teams build, deploy, observe or operate 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.