Data drift, where the statistical properties of live | AIGP
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Data drift, where the statistical properties of live: Which underlying cause best explains this pattern?

AIGP Understanding How to Govern AI Development Medium

Gradual post-launch accuracy loss from changing input distributions is data drift.

The question

Months after launch, a deployed demand-forecasting model steadily loses accuracy even though its code is unchanged; investigation shows that the distribution of incoming data no longer matches the training data. Which underlying cause best explains this pattern?

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  1. Data drift, where the statistical properties of live inputs diverge from those seen during training. ✓
    Gradual accuracy loss from shifting input distributions is classic data drift.
  2. Brittleness, where small perturbations to individual inputs cause sudden and disproportionate failures.
    Almost right as a failure mode, but brittleness is abrupt sensitivity to perturbations, not gradual distribution shift.
  3. Insufficient testing, where failure modes existed at launch because pre-release evaluation was too narrow.
    Almost right as a cause category, but the model performed well initially and degraded later, pointing to drift.
  4. Lack of quality data, where the original training set was too noisy or incomplete to learn the task well.
    Plausible, but poor initial data would show at launch, whereas here performance decayed after a good start.
The trap
Attributing gradual post-launch decay to launch-time causes (poor data, weak testing) instead of drift.

How to remember it

Gradual post-launch accuracy loss from changing input distributions is data drift.

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