Method: modelDeploymentMonitoringJobs.searchModelDeploymentMonitoringStatsAnomalies

Full name: projects.locations.modelDeploymentMonitoringJobs.searchModelDeploymentMonitoringStatsAnomalies

Searches Model Monitoring Statistics generated within a given time window.

Endpoint

post https://{service-endpoint}/v1/{modelDeploymentMonitoringJob}:searchModelDeploymentMonitoringStatsAnomalies

Where {service-endpoint} is one of the supported service endpoints.

Path parameters

modelDeploymentMonitoringJob string

Required. ModelDeploymentMonitoring Job resource name. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{modelDeploymentMonitoringJob}

Request body

The request body contains data with the following structure:

Fields
deployedModelId string

Required. The DeployedModel id of the [ModelDeploymentMonitoringObjectiveConfig.deployed_model_id].

featureDisplayName string

The feature display name. If specified, only return the stats belonging to this feature. Format: ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies.feature_display_name, example: "user_destination".

objectives[] object (StatsAnomaliesObjective)

Required. Objectives of the stats to retrieve.

pageSize integer

The standard list page size.

pageToken string

A page token received from a previous JobService.SearchModelDeploymentMonitoringStatsAnomalies call.

startTime string (Timestamp format)

The earliest timestamp of stats being generated. If not set, indicates fetching stats till the earliest possible one.

Uses RFC 3339, where generated output will always be Z-normalized and uses 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z", "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30".

endTime string (Timestamp format)

The latest timestamp of stats being generated. If not set, indicates feching stats till the latest possible one.

Uses RFC 3339, where generated output will always be Z-normalized and uses 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z", "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30".

Response body

Response message for JobService.SearchModelDeploymentMonitoringStatsAnomalies.

If successful, the response body contains data with the following structure:

Fields
monitoringStats[] object (ModelMonitoringStatsAnomalies)

Stats retrieved for requested objectives. There are at most 1000 ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies.prediction_stats in the response.

nextPageToken string

The page token that can be used by the next JobService.SearchModelDeploymentMonitoringStatsAnomalies call.

JSON representation 
{
  "monitoringStats": [
    {
      object (ModelMonitoringStatsAnomalies)
    }
  ],
  "nextPageToken": string
}

StatsAnomaliesObjective

Stats requested for specific objective.

Fields
type enum (ModelDeploymentMonitoringObjectiveType)
topFeatureCount integer

If set, all attribution scores between SearchModelDeploymentMonitoringStatsAnomaliesRequest.start_time and SearchModelDeploymentMonitoringStatsAnomaliesRequest.end_time are fetched, and page token doesn't take effect in this case. Only used to retrieve attribution score for the top Features which has the highest attribution score in the latest monitoring run.

JSON representation 
{
  "type": enum (ModelDeploymentMonitoringObjectiveType),
  "topFeatureCount": integer
}

ModelDeploymentMonitoringObjectiveType

The Model Monitoring Objective types.

Enums
MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED Default value, should not be set.
RAW_FEATURE_SKEW Raw feature values' stats to detect skew between Training-Prediction datasets.
RAW_FEATURE_DRIFT Raw feature values' stats to detect drift between Serving-Prediction datasets.
FEATURE_ATTRIBUTION_SKEW feature attribution scores to detect skew between Training-Prediction datasets.
FEATURE_ATTRIBUTION_DRIFT feature attribution scores to detect skew between Prediction datasets collected within different time windows.

ModelMonitoringStatsAnomalies

Statistics and anomalies generated by Model Monitoring.

Fields
objective enum (ModelDeploymentMonitoringObjectiveType)

Model Monitoring Objective those stats and anomalies belonging to.

deployedModelId string

Deployed Model id.

anomalyCount integer

Number of anomalies within all stats.

featureStats[] object (FeatureHistoricStatsAnomalies)

A list of historical Stats and Anomalies generated for all Features.

JSON representation 
{
  "objective": enum (ModelDeploymentMonitoringObjectiveType),
  "deployedModelId": string,
  "anomalyCount": integer,
  "featureStats": [
    {
      object (FeatureHistoricStatsAnomalies)
    }
  ]
}

FeatureHistoricStatsAnomalies

Historical Stats (and Anomalies) for a specific feature.

Fields
featureDisplayName string

Display name of the feature.

threshold object (ThresholdConfig)

Threshold for anomaly detection.

trainingStats object (FeatureStatsAnomaly)

Stats calculated for the Training Dataset.

predictionStats[] object (FeatureStatsAnomaly)

A list of historical stats generated by different time window's Prediction Dataset.

JSON representation 
{
  "featureDisplayName": string,
  "threshold": {
    object (ThresholdConfig)
  },
  "trainingStats": {
    object (FeatureStatsAnomaly)
  },
  "predictionStats": [
    {
      object (FeatureStatsAnomaly)
    }
  ]
}