This session explains a compact ML pipeline for ticket-driven signal engineering with data extraction from ticketing systems, text featurization, clustering and simple classifiers to surface preventive alerts. Session will discuss human-in-the-loop design, low-risk automation patterns and metrics for tracking deflection.

A short live notebook snippet will show clustering on sample ticket data and how a discovered pattern becomes a signal with a suggested playbook. Session will discuss how to start with limited data, validate improvements quickly and measure the operational impact that convinces leadership.