Chris Gilman

We all experience some form of notification fatigue. For manufacturing technicians, it is paramount to take action on alerts that are meaningful. We will share our journey in building a functional ML tool that automates the process of setting thresholds for industrial metric data to ensure users receive the right information at the right time.

We will discuss how to build a system of searches and models that allow for automated improvements for condition monitoring tools. Then, we will deep dive into functional logic that injects field experience and reasoning into statistical modeling for relevant results. We will also provide business value examples for OT implementations of Splunk monitoring industrial machine data.