This session highlights two anomaly detection use cases in production: identification of problematic life sciences manufacturing units and identification of significant newsworthy events. With both solutions, Shiny is integrated with live data to provide early detection for proactive intervention. Shiny’s intuitive user interface also allows for interaction with the data behind anomalies to uncover potential causes and paths to action or resolution.

The session also briefly highlights a rapid prototyping development approach with Shiny. This technique allows for collaborative refinement of the underlying anomaly detection model in R, quickly incorporating user feedback, where end users may not have in-depth machine learning knowledge.

Talk materials are available at https://docs.google.com/presentation/d/e/2PACX-1vTE7Ee2QIUGDUmfEKmF8l_WTQPVgnGaLJLGuuMquio57bXojeeb5YYSjuzO-xzYxMHxuX2cm_QNC2y-/pub?start=false&loop=false&delayms=60000&slide=id.gbb68c6dbe2_1_44.