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STAMM
What STAMM is
STAMM is an open-source framework for deploying and operating machine-learning soft sensors in industrial processes. It monitors live process data for concept drift and changes in operating regimes, and its dashboard brings together process measurements, sensor outputs, drift signals, and historical context. People can add labels through the dashboard for human review. A model registry records versions, configurations, artifacts, validation results, and metadata. STAMM serves Python and R soft sensors through REST inference; its event-driven orchestrator calls the registry over REST and links predictions to the data snapshot behind each result. The drift detector package includes 10 detectors behind a shared interface and can also be used separately. Data can enter through equipment REST hooks, MQTT applications, or frameworks such as LEAF. The reference time-series store is InfluxDB, while a PostgreSQL adapter is in progress. The project is licensed under Apache License 2.0 and offers self-hosted deployment. Its reference demo uses an industrial-scale penicillin fermentation simulator, with a curated dataset, Node-RED bioreactor, and working model registry. The project surfaces when maintenance may be needed, but does not prescribe how to rebuild a model.
Who it suits
STAMM is suited to process modelers and ML engineers deploying soft sensors alongside physical instruments in live industrial systems. Operators and project or production managers can use its dashboard to review process measurements, model outputs, and drift signals.
What it does well
- Open-source under Apache License 2.0.
- Monitors live data for drift and operating-regime changes.
- Registry tracks versions, artifacts, validation results, and metadata.
- Dashboard supports historical context and human-in-the-loop labelling.
- Ten drift detectors share a common interface.
- Self-hosted deployment is available.
What to know first
- A PostgreSQL adapter is still in progress.
- The framework flags possible maintenance needs but does not guide model rebuilding.
- Docker 24 or later and Docker Compose are required for the reference deployment.
- At least 8 GB RAM is recommended.
Limits
The reference deployment recommends 8 GB RAM or more and requires Docker 24 or later with Docker Compose. STAMM surfaces possible maintenance needs but does not prescribe model rebuilding; its PostgreSQL adapter is in progress.
Getting started
- Visit the STAMM project website.
- Prepare Linux, macOS, or Windows with Docker 24 or later and Docker Compose.
- Allow at least 8 GB of RAM for the recommended setup.
- Use the reference Docker Compose deployment.
- Explore the demo, which includes a fermentation simulator, curated dataset, Node-RED bioreactor, and model registry.
Verdict
Choose STAMM if you need an open-source, self-hosted framework to monitor industrial soft sensors and track model versions and results. Its drift signals can identify when maintenance may be needed, but the framework does not prescribe how to rebuild a model.
Questions
Is STAMM free?
Yes. It is listed as free and open source, and is released under Apache License 2.0.
Where can STAMM run?
Its listed platforms are API, Linux, macOS, self-hosted, web, and Windows. The reference Docker Compose installation supports Linux, macOS, or Windows and requires Docker 24 or later.
Which soft-sensor languages does it support?
The maker describes support for Python and R soft sensors, served through REST inference.
What does its model registry track?
It tracks model versions, configuration, artifacts, validation results, and metadata.
What data sources and storage does it describe?
Data can arrive through equipment REST hooks, MQTT applications, or frameworks such as LEAF. The reference time-series store is InfluxDB; a PostgreSQL adapter is in progress.
Who is the project contact?
The project page lists David Camilo Corrales at INRAE, Toulouse Biotechnology Institute, as a contact.
Facts from STAMM's own pages
- Audience
- The maker identifies process modelers, ML engineers, operators, and project or production managers as intended users. · stamm.inrae.fr, 4 Oct 2026
- Dashboard
- The dashboard shows live measurements, soft-sensor outputs, drift signals, and historical context, and supports human-in-the-loop labelling. · stamm.inrae.fr, 7 Oct 2026
- Data storage
- The reference time-series store uses InfluxDB; a PostgreSQL adapter is described as in progress. · stamm.inrae.fr, 7 Oct 2026
- Demo
- The reference demo applies STAMM to an industrial-scale penicillin fermentation simulator and includes a curated dataset, Node-RED bioreactor, and working model registry. · stamm.inrae.fr, 7 Oct 2026
- Deployment
- It integrates existing soft sensors into live systems alongside physical instruments without requiring rewrites. · stamm.inrae.fr, 7 Oct 2026
- Deployment requirements
- The documented Docker Compose installation supports Linux, macOS, or Windows with Docker 24 or later; 8 GB RAM or more is recommended. · github.com, 4 Oct 2026
- Drift detection
- It detects regime shifts and concept drift, including through a Python package with 10 detectors behind a single API. · stamm.inrae.fr, 7 Oct 2026
- Drift detectors
- The drift detector package provides 10 detectors through a common interface and can be used inside or outside STAMM. · stamm.inrae.fr, 4 Oct 2026
- Installation
- The reference deployment uses Docker Compose and lists Linux, macOS, or Windows with Docker 24 or later and Docker Compose as requirements. · github.com, 7 Oct 2026
- Integrations
- The workflow orchestrator calls the model registry over REST, and the project describes data ingestion through equipment REST hooks, MQTT applications, or frameworks such as LEAF. · github.com, 7 Oct 2026
- Intended users
- The site identifies process modelers, ML engineers, operators, and project leaders or process and production managers as intended users. · stamm.inrae.fr, 7 Oct 2026
- Language support
- The maker describes support for Python and R soft sensors, served through REST inference. · stamm.inrae.fr, 4 Oct 2026
- License
- STAMM is released under the Apache License 2.0. · github.com, 7 Oct 2026
- Model registry
- The registry tracks model versions, configuration, artifacts, validation results, and metadata; it supports Python and R models and REST inference. · stamm.inrae.fr, 7 Oct 2026
- Notable limitation
- STAMM surfaces when maintenance may be needed but does not prescribe how the model should be rebuilt. · stamm.inrae.fr, 7 Oct 2026
- Purpose
- STAMM is an open-source MLOps framework for deploying, monitoring, and maintaining machine-learning soft sensors in industrial processes. · stamm.inrae.fr, 7 Oct 2026
- Real-time monitoring
- It monitors live process data and detects concept drift and changes in operating regimes. · stamm.inrae.fr, 4 Oct 2026
- Security and compliance
- The project describes a FAIR-aligned YAML metadata schema for soft-sensor models that can link to FAIRDOM-SEEK catalogues such as the IBISBA Knowledge Hub. · github.com, 7 Oct 2026
- Support
- The project page lists David Camilo Corrales at INRAE, Toulouse Biotechnology Institute, as a contact. · github.com, 7 Oct 2026
- Workflow
- The event-driven orchestrator calls the model registry over REST and links predictions to the data snapshot that produced them. · stamm.inrae.fr, 4 Oct 2026