The model could advise. It could not control.
Manufacturing validation prohibited an autonomous change to machine state. Network segmentation meant inference had to continue offline, and every deployed model needed a traceable approval history.
The MES remained authoritative; predictions could only create advisory work items.
Plant networking could be unavailable during the exact period a failure developed.
Model, runtime and thresholds had to move through one controlled release.
A signed bundle crossed the plant boundary.
Failure windows joined to maintenance outcomes
Model, thresholds, schema and signature
Compare without changing operations
Offline scoring with bounded resources
Evidence and recommended inspection
Trust came from restraint.
The model proposed an inspection; a qualified operator remained responsible for action.
The signed bundle carried preprocessing and thresholds, preventing hidden edge drift.
Every version ran against live signals without surfacing alerts until acceptance criteria passed.
Earlier warning with no disruption to validated operations.
| Measure | Before | After |
|---|---|---|
| Unplanned downtime | Baseline | 38% lower |
| Median warning | Reactive | 7 minutes |
| Offline inference | Unavailable | Continuous |
| Model release evidence | Multiple records | Signed manifest |
Bring ML to the edge without losing control.
We can define the trust boundary, release evidence and safe fallback before model deployment.