detect_drift
Identify data distribution changes between training and test datasets to monitor model performance degradation over time.
Instructions
Detect data drift between a model's training data and test data.
Checks the result store first for precomputed drift results.
Falls back to on-the-fly computation if not found.
Numeric features are tested with PSI (primary) and KS (supporting).
Categorical features are tested with chi-squared.
Args:
model_id: ID of a registered model (e.g., "gbc_lubricant_quality").Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| model_id | Yes |