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STAMM

stamm.inrae.fr

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Spec score 7.1 #14 of 20 in Machine Learning Model Monitoring Software

What it is

STAMM is an open-source MLOps framework for deploying and maintaining machine-learning soft sensors in industrial processes. It monitors live process data for concept drift and changes in operating regimes, and its registry records model versions, configurations, artifacts, validation results, and metadata. A dashboard shows process measurements, soft-sensor outputs, drift signals, and historical context, with support for human-in-the-loop labelling. 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 that produced them. The drift detector package offers 10 detectors through a common interface and can also be used separately. Data can enter through equipment REST hooks, MQTT applications, or frameworks such as LEAF. InfluxDB is the reference time-series store; a PostgreSQL adapter is described as in progress. The reference Docker Compose installation supports Linux, macOS, or Windows with Docker 24 or later, and 8 GB RAM or more is recommended. Released under Apache License 2.0, STAMM is intended for process modelers, ML engineers, operators, and production managers.

Spec sheet

Purpose
STAMM is an open-source MLOps framework for deploying, monitoring, and maintaining machine-learning soft sensors in industrial processes.Read 7 Oct 2026 · stamm.inrae.fr
Intended users
The site identifies process modelers, ML engineers, operators, and project leaders or process and production managers as intended users.Read 7 Oct 2026 · stamm.inrae.fr
Audience
The maker identifies process modelers, ML engineers, operators, and project or production managers as intended users.Read 4 Oct 2026 · stamm.inrae.fr
Deployment
It integrates existing soft sensors into live systems alongside physical instruments without requiring rewrites.Read 7 Oct 2026 · stamm.inrae.fr
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.Read 7 Oct 2026 · github.com
Support
The project page lists David Camilo Corrales at INRAE, Toulouse Biotechnology Institute, as a contact.Read 7 Oct 2026 · github.com
License
STAMM is released under the Apache License 2.0.Read 7 Oct 2026 · github.com
Dashboard
The dashboard shows live measurements, soft-sensor outputs, drift signals, and historical context, and supports human-in-the-loop labelling.Read 7 Oct 2026 · stamm.inrae.fr
Data storage
The reference time-series store uses InfluxDB; a PostgreSQL adapter is described as in progress.Read 7 Oct 2026 · stamm.inrae.fr
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.Read 7 Oct 2026 · stamm.inrae.fr
Deployment requirements
The documented Docker Compose installation supports Linux, macOS, or Windows with Docker 24 or later; 8 GB RAM or more is recommended.Read 4 Oct 2026 · github.com
Drift detection
It detects regime shifts and concept drift, including through a Python package with 10 detectors behind a single API.Read 7 Oct 2026 · stamm.inrae.fr
Drift detectors
The drift detector package provides 10 detectors through a common interface and can be used inside or outside STAMM.Read 4 Oct 2026 · stamm.inrae.fr
Installation
The reference deployment uses Docker Compose and lists Linux, macOS, or Windows with Docker 24 or later and Docker Compose as requirements.Read 7 Oct 2026 · github.com
Language support
The maker describes support for Python and R soft sensors, served through REST inference.Read 4 Oct 2026 · stamm.inrae.fr
Model registry
The registry tracks model versions, configuration, artifacts, validation results, and metadata; it supports Python and R models and REST inference.Read 7 Oct 2026 · stamm.inrae.fr
Notable limitation
STAMM surfaces when maintenance may be needed but does not prescribe how the model should be rebuilt.Read 7 Oct 2026 · stamm.inrae.fr
Real-time monitoring
It monitors live process data and detects concept drift and changes in operating regimes.Read 4 Oct 2026 · stamm.inrae.fr
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.Read 7 Oct 2026 · github.com
Workflow
The event-driven orchestrator calls the model registry over REST and links predictions to the data snapshot that produced them.Read 4 Oct 2026 · stamm.inrae.fr

Strong points and limits

Strong points

  • Tracks drift and operating-regime changes in live data.
  • Registry records model versions, artifacts, and validation results.
  • Serves Python and R soft sensors through REST.
  • Ten drift detectors share a common interface.
  • Released under Apache License 2.0.

Limits

  • PostgreSQL adapter is still in progress.
  • Docker 24 or later is required for the reference installation.
  • At least 8 GB RAM is recommended.
  • It flags possible maintenance but does not prescribe model rebuilding.

Who it suits

STAMM is intended for process modelers, ML engineers, operators, and project or production managers working with industrial soft sensors. It suits teams that can deploy and operate a Docker Compose environment.

Verdict

STAMM brings model tracking, drift signals, and human review into an industrial soft-sensor workflow. Its self-hosted deployment and lack of guidance on rebuilding models are important fit considerations.

Where Specifiction places it

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