Modelinizin Altındaki Dünya Değiştiğinde

Your model is a snapshot of the past. As the real world moves on, that snapshot slowly goes out of date. This slow rot is called drift. 🌍

Data drift means the inputs change shape, like users suddenly being younger or shopping at new hours, even if the right answer rules stay the same.

Concept drift is deeper: the relationship between inputs and the target changes. The same input now deserves a different prediction than before.

A spam filter faces concept drift constantly. Spammers change tactics, so yesterday's clear spam signal becomes today's normal email. The concept moved.

Drift can be sudden, like a lockdown changing behavior overnight, or gradual, creeping over months. Both erode accuracy, just at different speeds.

Not every shift is permanent. Seasonal changes, like holiday shopping spikes, repeat each year and should not be mistaken for true drift.

To detect data drift, compare a feature's recent distribution against the training distribution and measure the gap between them.

Concept drift is harder because you need real labels. Watch your accuracy over time, and a steady decline hints the concept has shifted.

Drift is not broken code, it is the world doing its job. The only real cure is keeping your model fresh through retraining.

You cannot fix drift you never see. Set up drift monitoring first, then decide whether to alert, retrain, or roll back.

Remember the split: data drift is inputs changing, concept drift is the answer rule changing. Both signal it may be time to refresh the model.

Tell data drift and concept drift apart.

The world shifts under every model. Data drift changes inputs, concept drift changes the answer rule. Monitor both and retrain to stay accurate. ✅