service.ml_industrial()
Industrial ML projects rarely fail because of the model itself: they fall on vague use cases, ungoverned data and missing ownership after go-live.
The project starts from "we want to use ML" instead of "we want to improve this operational decision at this accuracy level".
Without a concrete operational decision as an anchor, any accuracy metric is self-referential and the business cannot use it.
The dataset is prepared with manual queries for the POC, and nobody rebuilds the pipeline when retraining or production is needed.
A versioned, reproducible data pipeline is the condition for a model to survive its first deploy.
The model runs but nobody is accountable for drift monitoring, updates or retirement: it becomes an invisible object.
An orphan model in production is a risk: it keeps influencing operational decisions while nobody can answer for its degradation.
Evaluation of available data quality and quantity. Identification of highest-impact use cases.
Extraction and transformation of relevant signals: vibrations, temperatures, consumption, process parameters.
Model training on historical data, cross-validation and testing on real data.
Production integration with real-time inference, drift monitoring and periodic retraining.
The algorithmic part matters, but it must live inside a sustainable technical and operational setup.
Sources, frequencies, transformations and assumptions required to make the model reliable.
Go-live mode, drift checks, ownership and lifecycle maintenance for the model.
Clear use cases, governed data, monitored deploy — no orphan models in production.
