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Open-source vector similarity search for Postgres.
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github.com
Vector database plugin for Postgres, written in Rust, specifically designed for LLM.
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Turns your ML code into microservices with web API, interactive GUI, and more.
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Open-source GenAI and LLM observability platform native to OpenTelemetry with traces and metrics. opensource.
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Neural networks framework in pure C: training and inference, no dependencies.
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A framework providing the right abstractions to ease research, development, and deployment of your ML pipelines.
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Machine learning model serving framework with dynamic batching and pipelined stages, provides an easy-to-use Python interface.
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Version and deploy your ML models following GitOps principles.
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A lightweight, portable pure C99 onnx inference engine for embedded devices with hardware acceleration support.
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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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Open-source all-in-one platform for engineering AI products. Traces, Evals, Datasets, Labels.
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Standardized Serverless ML Inference Platform on Kubernetes.
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Open source MLOps project that eases model handoffs between data scientist and DevOps.
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Kubernetes custom resource definition for serving ML models on arbitrary frameworks.
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Kubernetes-based system for hyperparameter tuning and neural architecture search.
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Build multimodal AI services via cloud native technologies · Model Serving · Generative AI · Neural Search · Cloud Native.
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A library for efficient similarity search and clustering of dense vectors.
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Eurybia monitors data and model drift over time and securizes model deployment with data validation.
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MII makes low-latency and high-throughput inference possible, powered by DeepSpeed.
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Curated list of awesome vector search framework/engine, library, cloud service and research papers to vector similarity search.
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An easy-to-use feature store. Optimized for time-series data.
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Deploy a ML inference service on a budget in less than 10 lines of code.
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Bayesian Inference Tools in Python.
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SciPy tutorials. This is outdated, check out scipy-lecture-notes.