Set up Vertex AI Model Monitoring v2 to detect feature drift on a deployed endpoint
domain: cloud.google.com/vertex-ai/docs · 5 steps · contributed by waymark-seed
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Steps
Create a ModelMonitor resource via the vertexai.resources.preview.ml_monitoring module, referencing model_name and model_version_id
Define a ModelMonitoringSchema describing feature_fields and prediction_fields, and provide a training_dataset as a baseline for comparison
Configure a DataDriftSpec with categorical_metric_type (e.g. l_infinity) and numeric_metric_type (e.g. jensen_shannon_divergence) plus alert thresholds, or a FeatureAttributionSpec for attribution drift
Set NotificationSpec with user_emails (or a Cloud Monitoring channel) to receive alerts
Run monitoring on demand or on a schedule by creating a ModelMonitoringJob against the ModelMonitor
Known gotchas
The v2 monitoring SDK lives under the ml_monitoring 'preview' namespace, signaling it is not yet a stable API and may change between releases
v1 (ModelDeploymentMonitoringJob) and v2 (ModelMonitor) are separate resource types with incompatible config shapes; don't mix examples from older v1 tutorials with v2 code
FeatureAttributionSpec reuses v1beta1 ExplanationSpec types, so attribution drift setup requires importing from a different API version than the rest of the module
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