You’ll usually see a gradual drop in real-world accuracy without any changes to the model itself.

Data drift occurs when the statistical properties of incoming data change over time. This is common in user behavior models, recommendation systems, and NLP pipelines where language evolves.

Start by monitoring feature distributions and comparing them to training-time baselines. Sudden shifts in mean, variance, or category frequency are strong indicators. Prediction confidence trends are also useful—models often become less confident before accuracy drops.

If drift is detected, retraining with recent data or introducing adaptive thresholds often restores performance.

Common mistakes:

• Monitoring only accuracy, not input features

• Using stale validation sets

• Ignoring seasonal or regional variations