Project background
Built on the success of earlier predictive maintenance work, the client wanted a standardized engine they could apply across plants with minimal per-site work — a productized rather than bespoke approach.
Challenge
Handling asset variety, per-site calibration, and model lifecycle management (drift, retraining) without turning every deployment into a research project.
Approach & solution
We packaged the engine around a feature store, a model registry, and standardized evaluation protocols. New sites onboard through a guided flow that bootstraps models from similar assets and refines them with local data over time.
Results & benefits
Deployment time for new sites dropped substantially, and the engine now runs across multiple plants with a much lighter ongoing support load than bespoke engagements. Model drift is caught and handled systematically.
