Intent-Driven Automation for Machine Data: Natural Language to Edge
summary
Claims a working pipeline that turns natural language into executable edge deployments using a chatbot, a knowledge graph (RDF/JSON‑LD, SHACL) and LLMs, with a RAG step to feed graph context into the model. The demo generates Docker commands and orchestration workflows that target cloud/edge machines and cuts deployment time from days to minutes. Built components include a chat/voice front end, RDF-backed ontology, automatic workflow synthesis and containerized deployment; project runs until end of next year with 16 partners.
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chapters (8)
- 0:00Introduction
- 1:08Intent-driven automation concept
- 3:17Natural language interaction
- 5:45Technology behind intent-driven automation
- 9:40Live demo: natural language interaction
- 16:39Project progress and future steps
- 17:40Benefits and impact
- 18:23Q&A
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