Data or model drift: Which term best describes it?
The gradual divergence of live inputs from the training distribution that erodes accuracy is drift.
The question
After launch, an image classifier's accuracy steadily declines as the real-world photos it receives gradually diverge from the images it was trained on. Working with cross-functional stakeholders, the team wants to name this contributing factor. Which term best describes it?
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- Data or model drift, where the live input distribution gradually diverges from the training distribution and degrades performance over time. ✓Drift is precisely the gradual divergence between production inputs and the training distribution that erodes accuracy after deployment.
- Brittleness, where the model fails abruptly on inputs that differ only slightly from those it handled correctly during its evaluation phase.Brittleness describes sudden failure on small perturbations, not the gradual distribution shift the scenario describes.
- Insufficient testing, where gaps in the pre-release test coverage allow defects to reach production and surface only once real users interact with the system.Insufficient testing is a process gap at build time; it does not name the ongoing divergence between input and training data.
- Lack of robustness, where the model cannot maintain performance under noisy, corrupted, or adversarially perturbed inputs encountered in operational conditions.Lack of robustness concerns resilience to noise or attacks, not the steady distributional divergence characteristic of drift.
The trap
Confusing gradual distribution shift (drift) with abrupt input-sensitivity failures (brittleness or lack of robustness). How to remember it
The gradual divergence of live inputs from the training distribution that erodes accuracy is drift.
How many of these would you get right?
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