STAMM for ML Engineers
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 — even as data, libraries, and processes change underneath them.
Why STAMM matters for you
Notebooks don't drift; production does. A model trained on six months of clean lab data lands in a plant where pumps wear, raw materials change, and somebody updates a controller without telling anyone. STAMM is built to be the layer between "the model passed validation" and "the model is still trustworthy this morning."
Wrap an existing soft sensor as a registered model — STAMM doesn't dictate framework, language, or training stack.
Statistical and structural drift checks run alongside live inference, raising signals only when they actually matter.
Every retrain becomes a tracked version with environment, metadata, and validation metrics — no silent overwrites.
Your journey with STAMM
Wire up the model
Package an existing soft sensor with its environment specs and register it through the Model Registry. Define the input schema, output variables, and the expected ranges that monitoring will check against.
Connect to live process data
Stream sensor measurements through the Database and let the Orchestrator drive inference, drift checks, and persistence end-to-end.
Monitor drift & performance
The Monitoring view exposes drift statistics, residual behavior, and operating-regime indicators in one place. Configure the thresholds that should turn into a maintenance signal.
Trigger maintenance with full context
When drift crosses thresholds, STAMM raises a maintenance signal with the relevant slice of data and metadata attached — so retraining decisions are evidence-based, not vibes-based.
Promote a new model version
Register the retrained model as a new version, validate side-by-side with the previous one, and switch traffic when ready. The history stays — for audit, rollback, and post-mortem.
What you bring to STAMM
You bring the deployment discipline — pipelines, environments, CI, retraining schedules. STAMM gives you a model lifecycle that's already wired for industrial processes: drift detection that respects regimes, a registry that keeps lineage honest, and an orchestrator that ties it together so you spend your time on engineering, not on plumbing.