Llmops
Showing 16 of 16 projects
Ready-to-deploy Docker templates for building real-time RAG, AI pipelines, and enterprise search applications with live data sync.
A CLI and library for evaluating, red-teaming, and comparing LLM prompts, agents, and RAGs with simple declarative configs.
An open-source platform for debugging, evaluating, and monitoring LLM applications, RAG systems, and agentic workflows with tracing and automated evaluations.
An open-source platform for debugging, evaluating, and monitoring LLM applications, RAG systems, and agentic workflows.
An open-source LLMOps platform unifying gateway, observability, evaluation, optimization, and experimentation for industrial-grade LLM applications.
A Python library for building production-ready model inference APIs, job queues, and multi-model serving systems for AI applications.
A Rust library for building scalable, modular, and ergonomic LLM-powered applications.
An open-source MLOps/LLMOps suite for experiment management, data management, pipelines, orchestration, scheduling, and model serving.
An AI-native proxy and data plane for agentic applications, providing built-in orchestration, safety, observability, and smart LLM routing.
An open-source MLOps platform for building, orchestrating, and deploying production AI pipelines and agents.
A curated collection of resources for building, training, serving, and optimizing production-grade Large Language Model applications.
An MLOps framework to package, deploy, monitor, and manage thousands of production machine learning models on Kubernetes.
An open-source LLMOps platform for prompt management, evaluation, and observability to build reliable LLM applications faster.
A free course teaching how to design, train, and deploy a production-ready real-time financial advisor LLM system using RAG and LLMOps.
An open-source platform for AI engineering with OpenTelemetry-native LLM observability, GPU monitoring, guardrails, evaluations, and prompt management.
A command-line tool for creating reproducible, container-based development environments for AI/ML workflows.
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