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Framework that implements AutoML algorithms for model architecture search at scale.
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Proof-of-concept OpenAI Gym environment for Neural Architecture Search (NAS).
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Ncnn is a high-performance neural network inference framework optimized for the mobile platform.
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Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
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Easy-to-use library to boost AI inference.
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A framework providing the right abstractions to ease research, development, and deployment of your ML pipelines.
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An Easy-to-Use and High-Performance AI deployment framework.
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Neural networks framework in pure C: training and inference, no dependencies.
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Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
github.com
Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
github.com
Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
github.com
Open-source GenAI and LLM observability platform native to OpenTelemetry with traces and metrics. opensource.
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Turns your ML code into microservices with web API, interactive GUI, and more.
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Vector database plugin for Postgres, written in Rust, specifically designed for LLM.
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Open-source vector similarity search for Postgres.
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Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
github.com
Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
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The open source solution for monitoring your AI models in production.
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OpenAI-compatible LLM inference server for Apple Silicon using MLX. 2-4x faster than Ollama with tool calling and prompt caching.
Examples showing how to use the OpenAI vision API to run inference on images, video files and webcam streams.
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Provides containers to encapsulate and deploy EdgeML pipelines and applications.
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MLOps framework to package, deploy, monitor and manage thousands of production machine learning models.
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Lets you create apps for your ML projects with deceptively simple Python scripts.
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Inference for text-embedding models.