With all the hype around AI/ML in observability, it's more likely than ever that companies benefit from storing and viewing data in one system and training ML models in another.
Organizations with complex distributed systems that span dozens of teams can have a hard time following such practice without burning out the teams owning the client-facing services. A typical solution is to have alerts on all the layers of their distributed systems. This approach almost always leads to an excessive number of alerts and results in alert fatigue.
Adaptive Paging is an alert handler that leverages the causality from tracing and OpenTracing's semantic conventions to page the team closest the problem. From a single alerting rule, a set of heuristics can be applied to identify the most probable cause, paging the respective team instead of the alert owner.
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