STAMM review
A focused self-hosted framework for monitoring soft sensors in live industrial processes.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
STAMM is an open-source MLOps framework for deploying, monitoring, and maintaining machine-learning soft sensors in live industrial processes. It is designed for process modelers, ML engineers, operators, and production managers working with industrial processes and digital twins. The framework can run existing soft sensors alongside physical instruments, while providing monitoring for feature drift, operating-regime drift, and divergence across co-deployed models. Its scope is a strong fit for teams that need to connect predictive models with ongoing process operations rather than manage a broad portfolio of unrelated machine-learning applications.
The monitoring and maintenance workflow is the central strength. STAMM includes a versioned model registry with metadata and traceability, a dashboard for live data, predictions, drift indicators, and labelling, plus time-series storage for reference and live process data. Event-driven workflow orchestration supports scoring and prediction storage, and REST model inference supports Python and R soft sensors. Together, these features address both operational visibility and the model lifecycle around soft-sensor deployments. The documented example uses a penicillin fermentation simulation, showing how the framework is presented for process-oriented scenarios.
STAMM is a particularly suitable choice for industrial teams that want an open-source, self-hosted framework centered on soft sensors, model drift, and live process data. InfluxDB and PostgreSQL integrations provide a defined fit for teams using those data systems, while Docker appears in the documented deployment examples. Teams seeking a broader monitoring product for varied machine-learning workloads may find the specialized focus less suitable. It also requires the adopting organization to manage its self-hosted deployment and surrounding infrastructure.
STAMM pros and cons
- Where it wins
- Detects feature, operating-regime, and model divergence drift
- Tracks model versions, metadata, and traceability in a registry
- Supports live dashboards, time-series data, and Python or R inference
- Where it doesn't
- Its focus is narrow compared with general-purpose model monitoring tools
- Self-hosted deployment places infrastructure responsibility on the team
- Documented integrations are centered on InfluxDB and PostgreSQL
STAMM fact sheet, pricing and score →
Advertiser disclosure: iTechGuides is reader-supported. Vendors can pay for top positions in our rankings and for a place on other products' pages, and we may earn a commission when you click some links. Bought places stand at the number bought; this page and our disclosure say so. Paying vendors may get a fuller, more detailed review from our editors, and that review can change their score. How we rank.
Last updated · How we research and update