# Model Drift

Model drift is a decline in an AI system’s usefulness when the real-world data, task, or behavior it encounters changes over time.

## Why it matters

A model can become less accurate when user behavior, source data, labels, or the task changes after deployment. Monitoring input distributions and real outcome quality helps teams distinguish drift from ordinary variance or an application bug.

## Example

A lead-scoring model is reevaluated after the company moves into a new market because the customer mix and conversion patterns no longer match its training period.

## FAQ

### What is Model Drift in simple terms?

Model drift is a decline in an AI system’s usefulness when the real-world data, task, or behavior it encounters changes over time.

### Why does Model Drift matter for teams using AI?

A model can become less accurate when user behavior, source data, labels, or the task changes after deployment. Monitoring input distributions and real outcome quality helps teams distinguish drift from ordinary variance or an application bug.

### What is a practical example of Model Drift?

A lead-scoring model is reevaluated after the company moves into a new market because the customer mix and conversion patterns no longer match its training period.


Source: https://www.luffy.so/ai-glossary/model-drift

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