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.
