STAMM: Soft sensor moniToring and mAintenance framework for Machine learning Models

STAMM is a comprehensive framework designed to facilitate the monitoring and maintenance of soft sensors powered by machine learning models. It ensures the reliability and accuracy of predictive systems by continuously tracking model performance, detecting drift, and enabling proactive maintenance. With a focus on industrial applications, STAMM helps optimise data-driven decision-making by providing real-time insights and automated adaptation mechanisms for evolving processes.

📄 STAMM Preprint

Suarez, Carlos; Astudillo, Alexander; Metcalfe, Brett; Crowther, Matthew; Koehorst, Jasper J.; Castillo, Esteban; Bize, Ariane and Corrales, David Camilo.
STAMM: Soft sensor moniToring and mAintenance framework for Machine learning Models.

Available at SSRN: https://ssrn.com/abstract=6054948
DOI: 10.2139/ssrn.6054948