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

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Abstract:

STAMM (Soft sensor moniToring and mAintenance framework for Machine learning Models) 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.

SEEK ID: https://hub.ibisba.eu/publications/18

DOI: 10.1016/j.softx.2026.102783

Projects: Bioindustry4.0 Public dissemination, Bioindustry4.0 Work Package 7: Tools for high-quality datasets, requisit...

Publication type: Journal

Journal: SoftwareX

Book Title: SoftwareX

Publisher: Elsevier BV

Citation: SoftwareX 35:102783.

Date Published: 1st Sep 2026

Registered Mode: by DOI

Authors: Carlos Suarez, Alexander Astudillo, Brett Metcalfe, Matthew Crowther, Jasper J. Koehorst, Esteban Castillo, Ariane Bize, David Camilo Corrales

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Citation
Suarez, C., Astudillo, A., Metcalfe, B., Crowther, M., Koehorst, J. J., Castillo, E., Bize, A., & Corrales, D. C. (2026). STAMM: Soft sensor moniToring and mAintenance framework for Machine learning Models. In SoftwareX (Vol. 35, p. 102783). Elsevier BV. https://doi.org/10.1016/j.softx.2026.102783
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Created: 16th Jun 2026 at 08:26

Last updated: 16th Jun 2026 at 08:28

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