Show HN: The platform layer for agentic ML engineering

Category: infrastructure

Tags: mlops, machine-learning, experiment-tracking, model-registry, ai-platform

Score: 5.8/10 (Innovation: 5, Technical: 7, Documentation: 6, Utility: 5)

LUML is a platform for managing the complete ML lifecycle, from experiment tracking to deployment, with a focus on data privacy by keeping storage and compute on user infrastructure. It integrates LLMOps, AgentOps, and MLOps into a single operational framework, which is a notable but not entirely novel approach. The project is interesting for teams seeking a unified, privacy-conscious alternative to fragmented ML toolchains.

Target audience: data engineers, ml engineers, devops, ai researchers

Repository: https://github.com/luml-ai/luml · Python · Apache-2.0 · 188 stars

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