STAMM: Soft sensor moniToring and mAintenance framework for Machine learning Models
Year: 2026
Type: software-paper
Abstract: STAMM ( S oft sensor moni T oring and m A intenance framework for M achine learning M odels) is an open-source MLOps framework for industrial machine-learning soft sensors. Unlike general-purpose MLOps platforms (MLflow, Kubeflow, Metaflow, ClearML), STAMM targets the regime where ground-truth labels arrive offline hours or days late, processes exhibit slow non-stationary dynamics, and models are multi-language. The framework comprises five loosely coupled components: a time-series database, workflow orchestrator, language-agnostic REST model registry, dashboard with human-in-the-loop labelling, and an extensible drift-detection package. STAMM was validated on an industrial-scale fed-batch penicillin fermentation (IndPenSim) with seven coexisting R and Python soft sensors served through the model registry.
Source: SoftwareX
Institutions: Universidad del Cauca, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Wageningen University & Research, Newcastle University, Tecnológico de Monterrey, Université Paris-Saclay, Centre National de la Recherche Scientifique, Institut National des Sciences Appliquées de Toulouse, Université de Toulouse
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,200
OpenAlex ID: W7164814591