Dexda leverages built-in ML-models for Alert de-dup and correlations. Those models are open and customizable. Additionally, it leverages NLP for summarization.
Great! So why would i have to build models based on CI properties matching with a specified percentage? I don’t have to do that with PagerDuty, which is how i get this functionality now. I just feed in the alerts, tell it which field in the alert to use as tenant id for multi-tenancy, and it just groups “similar” alerts. I train it after the fact if it gets it wrong by combining alerts or splitting grouped alerts manually. However, that’s just adding to the ML, not building out matching models/rules.
