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.
Tanya Cashorali is the founder of TCB Analytics, a boutique data and analytics consultancy. She hosts a world-wide community network of over 500 data enthusiasts, has helped universities launch data science programs, and is a frequent speaker at tech conferences.
