Would you like fries with that?
summary
An AI recommendation engine is implemented inside Red Hat’s Developer Hub built on Backstage.io using a Developer Hub analytics API, Knative event-driven microservices, OpenShift AI and ML-Ops pipelines the team ships a frontend plugin that surfaces thumbs up / down suggestions trained on a dataset of 1,250 interactions, 51 event chains and 65 action types the LSTM proof of concept hits 60% accuracy on synthetic data it's a functioning POC not production ready and needs real user signals to improve
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chapters (7)
- 0:00Developer productivity challenges
- 2:27Consolidating tools with platforms
- 3:51AI-driven developer workflows
- 5:15Predicting developer actions
- 7:46Comparing LSTM, HMM, and LLM models
- 12:25Model evaluation and improvements
- 13:28Open source and demo
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