Data is both constant and ever-changing. It’s a paradox — data holds patterns, truths that models can rely on, but at the same time, those patterns fade, twist, and morph. Data drift occurs when the invisible thread holding the data’s consistency starts to unravel. The model still tries to grasp the world it once knew, yet the world it was trained on no longer exists.
But how does something so stable — data — transform into something so unpredictable? Let’s unravel the enigma of data drift.
Data drift is the silent force that destabilizes machine learning models. It is the change that doesn’t announce itself, the transformation hiding beneath the surface. How can data drift, something so subtle, cause a model trained on the past to fail at predicting the present?
- Covariate Drift: The input data, once predictable, now shifts. The features that models once found so reliable are now behaving differently. A feature like age may have once predicted health outcomes with ease, but now the distribution of ages in a population has shifted, leaving the model confused.
- Prior Probability Shift: The target labels themselves can betray the model. What was once a binary world of clicks or no…