STAMM

6.8easy start · #13of 20
in Machine Learning Model Monitoring Software

Well documentedyes
Runs where you workyes

Runs on api, Linux, Mac, self-hosted, Web, Windows.

STAMM is ranked #13 of 20 in machine learning model monitoring software on The Geeks Club. It runs on API, Linux, macOS, Self-hosted, Web, Windows.

Compared on machine learning model monitoring software

Drift monitoring: Yes
Model performance metrics: Yes
Deployment options: self-hosted

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The record

Purpose: STAMM is an open-source MLOps framework for deploying, monitoring, and maintaining machine-learning soft sensors in industrial processes.
Real-time monitoring: It monitors live process data and detects concept drift and changes in operating regimes.
Model registry: The registry tracks model versions, configuration, artifacts, validation results, and metadata.
Language support: The maker describes support for Python and R soft sensors, served through REST inference.
Dashboard: The dashboard displays process measurements, soft-sensor outputs, drift signals, historical context, and supports human-in-the-loop labelling.
Data storage: The reference time-series store is InfluxDB; a PostgreSQL adapter is described as in progress.
Workflow: The event-driven orchestrator calls the model registry over REST and links predictions to the data snapshot that produced them.
Drift detectors: The drift detector package provides 10 detectors through a common interface and can be used inside or outside STAMM.
Integrations: The documented workflow accepts data through equipment REST hooks, MQTT applications, or frameworks such as LEAF; the demo uses a Node-RED emulator.
Deployment requirements: The documented Docker Compose installation supports Linux, macOS, or Windows with Docker 24 or later; 8 GB RAM or more is recommended.
License: STAMM is released under the Apache License 2.0.
Audience: The maker identifies process modelers, ML engineers, operators, and project or production managers as intended users.
Deployment: It integrates existing soft sensors into live systems alongside physical instruments without requiring rewrites.
Drift detection: It detects regime shifts and concept drift, including through a Python package with 10 detectors behind a single API.

Where it ranks on The Geeks Club

Best Machine Learning Model Monitoring Software in 2026#13 of 20

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Sources

stamm.inrae.fr· checked 4 Oct 2026
stamm.inrae.fr/docs/intro/· checked 4 Oct 2026
github.com/stamm-4m· checked 4 Oct 2026