Question

You operate a deployed churn-prediction model on Vertex AI. Your monitoring shows that the statistical distribution of the incoming feature values has remained stable over the past three months (no input feature skew or drift detected). However, business stakeholders report that the model's live accuracy, measured against ground-truth churn labels that arrive 30 days later, has steadily declined from 0.88 to 0.71. What is the most likely cause of this degradation?

Accepted Answer

Concept drift — the relationship between the input features and the target label has changed even though input distributions are unchanged

Answer

Training-serving skew caused by inconsistent preprocessing between the training pipeline and the serving endpoint

Answer

Prediction drift caused by a shift in the distribution of the model's output scores

Answer

Data drift caused by a change in the input feature distributions over time