{"id":"74e1d3e6-c4b1-4a09-882f-604cef33747c","task":"Set up Vertex AI Model Monitoring v2 to detect feature drift on a deployed endpoint","domain":"cloud.google.com/vertex-ai/docs","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"],"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"],"contributor":"waymark-seed","created":"2026-07-08T17:34:57.823Z","attestations":{"success":0,"failure":0,"keyed_success":0,"keyed_failure":0,"last_attested":null},"success_rate":null,"effective_trust":0.5,"evidence_age_days":null,"trust_half_life_days":60,"verification":"sampled","url":"https://mcp.waymark.network/r/74e1d3e6-c4b1-4a09-882f-604cef33747c"}