Neural network inference from the command line, implemented in CHICKEN Scheme.
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Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
LLM Ops platform with Analytics, Monitoring, Evaluations and an LLM Optimization Studio powered by DSPy.
Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!
Open-source all-in-one platform for engineering AI products. Traces, Evals, Datasets, Labels.
Standardized Serverless ML Inference Platform on Kubernetes.
Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
Open source MLOps project that eases model handoffs between data scientist and DevOps.
Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
Kubernetes custom resource definition for serving ML models on arbitrary frameworks.
Kubernetes-based system for hyperparameter tuning and neural architecture search.
Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
Build multimodal AI services via cloud native technologies · Model Serving · Generative AI · Neural Search · Cloud Native.
Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
Create customizable UI components around your models.
Go binding for MXNet c predict api to do inference with a pre-trained model.
Production-grade SDK for observability, automated evaluations and prompt management with sub-100ms guardrails for LLM/agent workflows.
Open-source self-hostable end-to-end LLMOps platform unifying tracing, evals, simulations, datasets, gateway, and guardrails.
A Virtual Feature Store. Turn your existing data infrastructure into a feature store.
An enterprise-grade, high performance feature store.
FeatherCNN is a high performance inference engine for convolutional neural networks.
Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
A library for efficient similarity search and clustering of dense vectors.
Interactive reports to analyze ML models during validation or production monitoring.