KMDS is a complete methodology framework for building well-documented analytical and machine learning models for operational data. It combines decision guidance at every analytical phase—data cleaning, featurization, and modeling—with LLM-assisted knowledge capture and semantic search.
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Maker
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Hey Product Hunt community! 👋As data teams, we have all been there: a data scientist leaves the company, or you revisit a project after six months, and the research trail is completely cold. You are left staring at fragmented Jupyter notebooks, wondering why a specific modeling decision or data engineering choice was made.We built KMDS (Knowledge Management for Data Science) to solve this exact problem. It is an open-source, ontology-backed ecosystem designed to capture, organize, and reuse insights from your data science experiments.Key Features:Local Repo Scanning: Use local LLMs acting as specialized personas (Data Scientist, Tech Lead, Architect) to auto-generate structured knowledge graphs from your codebase.Interactive UI Workbench: Visually audit, explore, and safely edit your knowledge graphs without messing up raw files.Natural Language Ingestion: Simply describe your experimental insights in plain English, and KMDS maps them into the ontology.Semantic AI Search: Query your team's historical findings using local vector indices powered by Ollama.KMDS runs entirely on your local machine to keep your data and IP secure.Check out our GitHub repository, give it a spin, and let us know your thoughts! What features or integrations should we build next? We would love your feedback! 👇
There is now a significantly improved version with a completely documented example, please see:
https://github.com/rajivsam/dd_parser_cleaner_migration/blob/main/sba_migration/documents/KMDS_toolkit_summary.md