Just trying to understand if the library use case really needed AI. It sounded like a rules-based workflow engine to monitor performance, create tickets if SLAs weren’t met with details of the issue etc., even at scale.
Good point, Krishnan. There are 2 points that move beyond a rules-based workflow. The first is the connection between experience and metrics. Data from tickets and troubleshooting are necessary to define "bad experience." But you're right, once that analysis is complete, thresholds can be set and that part becomes rule-based. The second point is the root cause analysis and automatic logging of supporting information about the event. That requires models.
Thanks, Vin.
Just trying to understand if the library use case really needed AI. It sounded like a rules-based workflow engine to monitor performance, create tickets if SLAs weren’t met with details of the issue etc., even at scale.
Good point, Krishnan. There are 2 points that move beyond a rules-based workflow. The first is the connection between experience and metrics. Data from tickets and troubleshooting are necessary to define "bad experience." But you're right, once that analysis is complete, thresholds can be set and that part becomes rule-based. The second point is the root cause analysis and automatic logging of supporting information about the event. That requires models.