Establish continuous performance monitoring with a regular: Which post-deployment governance practice most
Gradual accuracy loss from drift is caught by continuous monitoring paired with a scheduled maintenance and retraining cadence.
The question
A deployed credit-scoring model's accuracy has quietly declined as customer behavior shifted over eighteen months, but no one noticed until complaints rose. Which post-deployment governance practice most directly addresses this failure going forward?
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- Complete a fresh pre-deployment impact assessment reviewing anticipated risks before the model may re-enter the production environmentImpact assessment is a valuable pre-deployment control, but it is a point-in-time review and does not by itself detect gradual live-performance drift.
- Expand the vendor licensing agreement to add stronger indemnity and warranty terms covering periods of degraded model accuracyContractual terms allocate liability but do nothing to detect or correct the operational drift causing the accuracy decline.
- Establish continuous performance monitoring with a regular schedule for maintenance, updates and retraining triggered by drift ✓Ongoing monitoring plus a defined retraining cadence catches degradation from data or concept drift before harm accumulates, which is exactly the gap here.
- Publish an external communication plan so affected customers understand how the credit scoring decisions are generatedTransparency communication is worthwhile, yet it neither monitors performance nor triggers the retraining that resolves the drift.
The trap
Believing a thorough pre-deployment assessment removes the need for ongoing performance monitoring. How to remember it
Gradual accuracy loss from drift is caught by continuous monitoring paired with a scheduled maintenance and retraining cadence.
How many of these would you get right?
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