KarthikSriramGit/H.E.I.M.D.A.L.L
H.E.I.M.D.A.L.L looks at fleet telemetry and gives you natural-language insights. GPU data loading (cuDF), local LLM inference (Gemma 2), and production NIM on GKE. Open the notebooks, run cells, get answers! Quick start should not take longer than 10 minutes and the T4 path is completely free!
This project helps operations managers, fleet supervisors, or robotics engineers analyze vast amounts of telemetry data from fleets of autonomous vehicles or robots. You feed in raw telemetry streams, and the system processes it to answer natural-language questions about fleet performance or anomalies. The output is specific vehicle or robot IDs, timestamps, and relevant metrics, eliminating the need to write complex data queries.
Use this if you need to quickly get insights from large datasets of robot or vehicle telemetry using natural language, without manual data querying.
Not ideal if you only have small, infrequent telemetry data or prefer traditional dashboarding and querying methods.
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Last pushed
Mar 07, 2026
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