Open-source · PublicationsKeep your soft sensors reliable in production.
STAMM is an open-source MLOps framework for monitoring and maintaining machine-learning soft sensors in live industrial processes — detecting drift, tracking versions, and keeping your digital twin aligned with the real world.
An operational layer for your soft sensors.
Moving ML models into production is not trivial. STAMM treats your soft sensor as a living component of the process — tracking data, monitoring drift, and surfacing when maintenance is needed, without prescribing how the model should be rebuilt.
Real-time deployment
Integrate existing soft sensors into live systems alongside physical instruments — no rewrites required.
Drift detection
Catch gradual or abrupt changes in operating regimes before they silently degrade prediction quality.
Model registry
Track multiple versions of the same model across deployments with full traceability and metadata.
Dashboard
Provides real-time visualisation, drift monitoring and human-in-the-loop labelling.
The soft-sensor lifecycle, managed end-to-end.
From the moment a soft sensor is deployed, STAMM watches it like an experienced operator — tracking inputs, stability, and outputs so you can trust what the model is telling you.
Deploy
Plug an existing soft sensor into live process data.
Observe
Stream measurements alongside hidden variable estimates.
Detect
Identify regime shifts and concept drift in real time.
Signal
Raise maintenance flags only when it actually matters.
Evolve
Track versions over time for continuity and traceability.
Built for the people who keep models honest in production.
STAMM sits between the lab and the factory floor. Whether you build the models, run the process, or supervise the digital twin, STAMM gives you a shared, observable layer to work from.
Process Modelers
Experts in Process Engineering who design and validate soft sensors using mechanistic or hybrid models. They ensure predictions remain physically and biologically meaningful, and interpret model behavior as process conditions evolve.
See their journey →
ML engineers
Specialists in MLOps who deploy, monitor, and maintain soft sensors in production. They manage pipelines, versioning, and retraining, ensuring models run reliably over time.
See their journey →
Operators
Frontline users who run the process in real time. They rely on soft sensor outputs for day-to-day decisions and need clear, immediate indicators of trust and stability.
See their journey →
Project Leaders & Process/Production Managers
Responsible for overall process outcomes, timelines, and risk. They use high-level insights from systems like STAMM to ensure reliable operation, support decision-making, and maintain alignment between models and the real process.
See their journey →A modular architecture, built to extend.
Each STAMM module is specialized for a single concern and communicates through clean APIs, so you can update, replace, or integrate pieces without rewiring the whole system.
Model Registry
Store, version, and retrieve soft sensor models with structured metadata for inputs, outputs, training setup, and validation.
Explore model registry →Dashboard
Interactive monitoring for data sources, soft sensors, drift detection, and simulation assessment.
Explore dashboard →Database
Time-series storage for process data and model outputs, tuned for live industrial workloads.
Explore database →Orchestrator
Coordinates STAMM's modules end-to-end so deployment, monitoring, and maintenance stay in sync.
Explore orchestrator →Try STAMM on IndPenSim.
Our reference demo applies STAMM to an industrial-scale penicillin fermentation simulator — with a Node-RED bioreactor, curated dataset, and working model registry.
Papers behind STAMM.
The STAMM preprint explains each component in detail. The publications below describe models that ship in the demo model registry.
Built by engineers and researchers across Europe and Latin America.
Developed as part of the European project BioIndustry 4.0 — with equal contributions across design, development, and refinement.
Team
Collaborators
Contributing domain expertise, review, and institutional support.
Alumni
Past contributors who shaped STAMM and have since moved on.
In collaboration with
STAMM is developed under the BioIndustry 4.0 European project and the IBISBA Research Infrastructure.
