Capability 08 · Vision AI
The demo worked.
The line is harder.
A deployment flow for vision models that need to keep working on a real line — as lighting shifts, dust settles, and edge cases arrive daily.
The situation
Accuracy in a demo and accuracy in production are different problems.
The model worked on clean test images. On the floor, the lighting changes through the day, dust settles on the lens, and new edge cases appear.
The hardware is limited, the network is unreliable, and results that arrive too late can’t be acted on.
A model that was accurate last month can quietly drift without anyone noticing.
The challenge
Staying accurate is the hard part — and noticing when you're not.
Getting a model to production is one thing. Keeping it accurate as conditions change is another.
It has to run reliably on edge hardware, feed results back fast enough to matter, and be watched so drift is caught early.
Without monitoring, the first sign of trouble is a missed defect or a storm of false alarms.
The capability
We take the model to the floor — and keep it accurate.
The model runs on edge hardware, checks the visual input locally, and sends structured results back into the operational system in real time.
Performance is monitored over time, so drift is caught before it becomes a problem, and the model is retrained as conditions change.
Inspection becomes part of the loop, not a separate step that ages out.
How the flow works
Before / after
A model that worked in a demo struggles on the line. Results are delayed, and drift goes unnoticed until something is missed.
The model runs at the edge, reports back in real time, and is monitored so drift is caught early and corrected. It keeps working as conditions change, not just at launch.
Controls
- Local processing on the edge
- Results logged and returned to the system
- A confidence threshold for review
- Human review for uncertain cases
- Performance monitored for drift
- Alerts when accuracy slips
- Retraining as conditions change
- A clear owner for the deployed model
Where this applies
What changes
Questions about vision in production
What happens when the system is not sure?
It asks. Uncertain cases go to a person to review and approve instead of being decided automatically. The line keeps moving and the judgement calls stay with your team.