Continuous monitoring with a scheduled retraining: Which ongoing governance practice most directly addresses
Drift-driven accuracy loss is addressed by continuous monitoring plus a scheduled cadence of maintenance, updates and retraining.
The question
Six months after launch, a demand-forecasting model's accuracy is slipping because customer behavior has shifted away from the patterns in its training data. Which ongoing governance practice most directly addresses this degradation?
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- A one-time pre-deployment impact assessment of the selected modelA valid earlier step, but a single pre-launch assessment cannot correct accuracy that drifts months after go-live.
- An external communication plan describing the outage to customersSometimes useful, yet communicating about degradation does not restore the model's accuracy against shifting data.
- A licensing review confirming the contractually permitted deployment scopeA legitimate due-diligence task, but contract scope is unrelated to correcting drift-induced performance decline.
- Continuous monitoring with a scheduled retraining and update cadence ✓Detecting drift-driven accuracy loss and scheduling retraining is exactly continuous monitoring paired with a maintenance cadence.
The trap
Believing a single pre-deployment assessment substitutes for ongoing monitoring and scheduled retraining once data drifts. How to remember it
Drift-driven accuracy loss is addressed by continuous monitoring plus a scheduled cadence of maintenance, updates and retraining.
How many of these would you get right?
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Every answer, right and wrong, comes with its own explanation.