A machine learning engineer is monitoring a deployed model for data drift. The input features are a mix of categorical and numerical columns. The baseline is from the training data. Which SageMaker Model Monitor feature should they enable to detect changes in the distribution of each feature over time?
Trap 1: Bias drift monitoring
Bias drift monitoring detects changes in fairness metrics, not the distribution of individual input features.
Trap 2: Model quality monitoring
Model quality monitoring tracks metrics like accuracy or precision using ground truth labels, not input feature distributions.
Trap 3: Feature attribution drift monitoring
Feature attribution drift monitors changes in SHAP values over time, not the raw feature distributions.
- A
Bias drift monitoring
Why wrong: Bias drift monitoring detects changes in fairness metrics, not the distribution of individual input features.
- B
Data quality monitoring
Data quality monitoring compares the distributions of input features against a baseline to detect statistical and schema drift.
- C
Model quality monitoring
Why wrong: Model quality monitoring tracks metrics like accuracy or precision using ground truth labels, not input feature distributions.
- D
Feature attribution drift monitoring
Why wrong: Feature attribution drift monitors changes in SHAP values over time, not the raw feature distributions.