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A library for efficient similarity search and clustering of dense vectors.
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FeatherCNN is a high performance inference engine for convolutional neural networks.
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An enterprise-grade, high performance feature store.
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A Virtual Feature Store. Turn your existing data infrastructure into a feature store.
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Open-source self-hostable end-to-end LLMOps platform unifying tracing, evals, simulations, datasets, gateway, and guardrails.
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Production-grade SDK for observability, automated evaluations and prompt management with sub-100ms guardrails for LLM/agent workflows.
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GEO/AEO Tracker helps monitor local SEO visibility, map rankings, business listings, and local search performance.
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Go binding for MXNet c predict api to do inference with a pre-trained model.
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Create customizable UI components around your models.
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Build multimodal AI services via cloud native technologies · Model Serving · Generative AI · Neural Search · Cloud Native.
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Kubernetes-based system for hyperparameter tuning and neural architecture search.
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Kubernetes custom resource definition for serving ML models on arbitrary frameworks.
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Open source MLOps project that eases model handoffs between data scientist and DevOps.
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Standardized Serverless ML Inference Platform on Kubernetes.
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LabNotebook is a tool that allows you to flexibly monitor, record, save, and query all your machine learning experiments.
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Open-source all-in-one platform for engineering AI products. Traces, Evals, Datasets, Labels.
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Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!
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Neural network inference from the command line, implemented in CHICKEN Scheme.
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linkinator helps monitor local SEO visibility, map rankings, business listings, and local search performance.
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mcp-gsc helps monitor indexing, search performance, crawl data, and site visibility.
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Version and deploy your ML models following GitOps principles.
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Open source MLOps platform that helps you collaborate, reproduce and share your ML work.
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Generic mechanism for data scientists to build, run, and monitor ML tasks and pipelines.
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Machine learning model serving framework with dynamic batching and pipelined stages, provides an easy-to-use Python interface.