---
title: Getting Started with Datadog
description: Datadog, the leading service for cloud-scale monitoring.
breadcrumbs: Docs > Infrastructure > Datadog Resource Catalog
---

# gcp_aiplatform_model_deployment_monitoring_job{% #gcp_aiplatform_model_deployment_monitoring_job %}

## `analysis_instance_schema_uri`{% #analysis_instance_schema_uri %}

**Type**: `STRING`**Provider name**: `analysisInstanceSchemaUri`**Description**: YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If this field is empty, all the feature data types are inferred from predict_instance_schema_uri, meaning that TFDV will use the data in the exact format(data type) as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.

## `ancestors`{% #ancestors %}

**Type**: `UNORDERED_LIST_STRING`

## `bigquery_tables`{% #bigquery_tables %}

**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `bigqueryTables`**Description**: Output only. The created bigquery tables for the job under customer project. Customer could do their own query & analysis. There could be 4 log tables in maximum: 1. Training data logging predict request/response 2. Serving data logging predict request/response

- `bigquery_table_path`**Type**: `STRING`**Provider name**: `bigqueryTablePath`**Description**: The created BigQuery table to store logs. Customer could do their own query & analysis. Format: `bq://.model_deployment_monitoring_._`
- `log_source`**Type**: `STRING`**Provider name**: `logSource`**Description**: The source of log.**Possible values**:
  - `LOG_SOURCE_UNSPECIFIED` - Unspecified source.
  - `TRAINING` - Logs coming from Training dataset.
  - `SERVING` - Logs coming from Serving traffic.
- `log_type`**Type**: `STRING`**Provider name**: `logType`**Description**: The type of log.**Possible values**:
  - `LOG_TYPE_UNSPECIFIED` - Unspecified type.
  - `PREDICT` - Predict logs.
  - `EXPLAIN` - Explain logs.
- `request_response_logging_schema_version`**Type**: `STRING`**Provider name**: `requestResponseLoggingSchemaVersion`**Description**: Output only. The schema version of the request/response logging BigQuery table. Default to v1 if unset.

## `create_time`{% #create_time %}

**Type**: `TIMESTAMP`**Provider name**: `createTime`**Description**: Output only. Timestamp when this ModelDeploymentMonitoringJob was created.

## `enable_monitoring_pipeline_logs`{% #enable_monitoring_pipeline_logs %}

**Type**: `BOOLEAN`**Provider name**: `enableMonitoringPipelineLogs`**Description**: If true, the scheduled monitoring pipeline logs are sent to Google Cloud Logging, including pipeline status and anomalies detected. Please note the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging#pricing).

## `encryption_spec`{% #encryption_spec %}

**Type**: `STRUCT`**Provider name**: `encryptionSpec`**Description**: Customer-managed encryption key spec for a ModelDeploymentMonitoringJob. If set, this ModelDeploymentMonitoringJob and all sub-resources of this ModelDeploymentMonitoringJob will be secured by this key.

- `kms_key_name`**Type**: `STRING`**Provider name**: `kmsKeyName`**Description**: Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.

## `endpoint`{% #endpoint %}

**Type**: `STRING`**Provider name**: `endpoint`**Description**: Required. Endpoint resource name. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`

## `error`{% #error %}

**Type**: `STRUCT`**Provider name**: `error`**Description**: Output only. Only populated when the job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.

- `code`**Type**: `INT32`**Provider name**: `code`**Description**: The status code, which should be an enum value of google.rpc.Code.
- `message`**Type**: `STRING`**Provider name**: `message`**Description**: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

## `gcp_display_name`{% #gcp_display_name %}

**Type**: `STRING`**Provider name**: `displayName`**Description**: Required. The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.

## `labels`{% #labels %}

**Type**: `UNORDERED_LIST_STRING`

## `latest_monitoring_pipeline_metadata`{% #latest_monitoring_pipeline_metadata %}

**Type**: `STRUCT`**Provider name**: `latestMonitoringPipelineMetadata`**Description**: Output only. Latest triggered monitoring pipeline metadata.

- `gcp_status`**Type**: `STRUCT`**Provider name**: `status`**Description**: The status of the most recent monitoring pipeline.
  - `code`**Type**: `INT32`**Provider name**: `code`**Description**: The status code, which should be an enum value of google.rpc.Code.
  - `message`**Type**: `STRING`**Provider name**: `message`**Description**: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
- `run_time`**Type**: `TIMESTAMP`**Provider name**: `runTime`**Description**: The time that most recent monitoring pipelines that is related to this run.

## `log_ttl`{% #log_ttl %}

**Type**: `STRING`**Provider name**: `logTtl`**Description**: The TTL of BigQuery tables in user projects which stores logs. A day is the basic unit of the TTL and we take the ceil of TTL/86400(a day). e.g. { second: 3600} indicates ttl = 1 day.

## `logging_sampling_strategy`{% #logging_sampling_strategy %}

**Type**: `STRUCT`**Provider name**: `loggingSamplingStrategy`**Description**: Required. Sample Strategy for logging.

- `random_sample_config`**Type**: `STRUCT`**Provider name**: `randomSampleConfig`**Description**: Random sample config. Will support more sampling strategies later.
  - `sample_rate`**Type**: `DOUBLE`**Provider name**: `sampleRate`**Description**: Sample rate (0, 1]

## `model_deployment_monitoring_objective_configs`{% #model_deployment_monitoring_objective_configs %}

**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `modelDeploymentMonitoringObjectiveConfigs`**Description**: Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.

- `deployed_model_id`**Type**: `STRING`**Provider name**: `deployedModelId`**Description**: The DeployedModel ID of the objective config.
- `objective_config`**Type**: `STRUCT`**Provider name**: `objectiveConfig`**Description**: The objective config of for the modelmonitoring job of this deployed model.
  - `explanation_config`**Type**: `STRUCT`**Provider name**: `explanationConfig`**Description**: The config for integrating with Vertex Explainable AI.
    - `enable_feature_attributes`**Type**: `BOOLEAN`**Provider name**: `enableFeatureAttributes`**Description**: If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
    - `explanation_baseline`**Type**: `STRUCT`**Provider name**: `explanationBaseline`**Description**: Predictions generated by the BatchPredictionJob using baseline dataset.
      - `bigquery`**Type**: `STRUCT`**Provider name**: `bigquery`**Description**: BigQuery location for BatchExplain output.
        - `output_uri`**Type**: `STRING`**Provider name**: `outputUri`**Description**: Required. BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: `bq://projectId` or `bq://projectId.bqDatasetId` or `bq://projectId.bqDatasetId.bqTableId`.
      - `gcs`**Type**: `STRUCT`**Provider name**: `gcs`**Description**: Cloud Storage location for BatchExplain output.
        - `output_uri_prefix`**Type**: `STRING`**Provider name**: `outputUriPrefix`**Description**: Required. Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
      - `prediction_format`**Type**: `STRING`**Provider name**: `predictionFormat`**Description**: The storage format of the predictions generated BatchPrediction job.**Possible values**:
        - `PREDICTION_FORMAT_UNSPECIFIED` - Should not be set.
        - `JSONL` - Predictions are in JSONL files.
        - `BIGQUERY` - Predictions are in BigQuery.
  - `prediction_drift_detection_config`**Type**: `STRUCT`**Provider name**: `predictionDriftDetectionConfig`**Description**: The config for drift of prediction data.
    - `default_drift_threshold`**Type**: `STRUCT`**Provider name**: `defaultDriftThreshold`**Description**: Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
      - `value`**Type**: `DOUBLE`**Provider name**: `value`**Description**: Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance: 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
  - `training_dataset`**Type**: `STRUCT`**Provider name**: `trainingDataset`**Description**: Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
    - `bigquery_source`**Type**: `STRUCT`**Provider name**: `bigquerySource`**Description**: The BigQuery table of the unmanaged Dataset used to train this Model.
      - `input_uri`**Type**: `STRING`**Provider name**: `inputUri`**Description**: Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`.
    - `data_format`**Type**: `STRING`**Provider name**: `dataFormat`**Description**: Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
    - `dataset`**Type**: `STRING`**Provider name**: `dataset`**Description**: The resource name of the Dataset used to train this Model.
    - `gcs_source`**Type**: `STRUCT`**Provider name**: `gcsSource`**Description**: The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
      - `uris`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `uris`**Description**: Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see [https://cloud.google.com/storage/docs/wildcards](https://cloud.google.com/storage/docs/wildcards).
    - `logging_sampling_strategy`**Type**: `STRUCT`**Provider name**: `loggingSamplingStrategy`**Description**: Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
      - `random_sample_config`**Type**: `STRUCT`**Provider name**: `randomSampleConfig`**Description**: Random sample config. Will support more sampling strategies later.
        - `sample_rate`**Type**: `DOUBLE`**Provider name**: `sampleRate`**Description**: Sample rate (0, 1]
    - `target_field`**Type**: `STRING`**Provider name**: `targetField`**Description**: The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
  - `training_prediction_skew_detection_config`**Type**: `STRUCT`**Provider name**: `trainingPredictionSkewDetectionConfig`**Description**: The config for skew between training data and prediction data.
    - `default_skew_threshold`**Type**: `STRUCT`**Provider name**: `defaultSkewThreshold`**Description**: Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
      - `value`**Type**: `DOUBLE`**Provider name**: `value`**Description**: Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance: 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.

## `model_deployment_monitoring_schedule_config`{% #model_deployment_monitoring_schedule_config %}

**Type**: `STRUCT`**Provider name**: `modelDeploymentMonitoringScheduleConfig`**Description**: Required. Schedule config for running the monitoring job.

- `monitor_interval`**Type**: `STRING`**Provider name**: `monitorInterval`**Description**: Required. The model monitoring job scheduling interval. It will be rounded up to next full hour. This defines how often the monitoring jobs are triggered.
- `monitor_window`**Type**: `STRING`**Provider name**: `monitorWindow`**Description**: The time window of the prediction data being included in each prediction dataset. This window specifies how long the data should be collected from historical model results for each run. If not set, ModelDeploymentMonitoringScheduleConfig.monitor_interval will be used. e.g. If currently the cutoff time is 2022-01-08 14:30:00 and the monitor_window is set to be 3600, then data from 2022-01-08 13:30:00 to 2022-01-08 14:30:00 will be retrieved and aggregated to calculate the monitoring statistics.

## `model_monitoring_alert_config`{% #model_monitoring_alert_config %}

**Type**: `STRUCT`**Provider name**: `modelMonitoringAlertConfig`**Description**: Alert config for model monitoring.

- `email_alert_config`**Type**: `STRUCT`**Provider name**: `emailAlertConfig`**Description**: Email alert config.
  - `user_emails`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `userEmails`**Description**: The email addresses to send the alert.
- `enable_logging`**Type**: `BOOLEAN`**Provider name**: `enableLogging`**Description**: Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto ModelMonitoringStatsAnomalies. This can be further synced to Pub/Sub or any other services supported by Cloud Logging.
- `notification_channels`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `notificationChannels`**Description**: Resource names of the NotificationChannels to send alert. Must be of the format `projects//notificationChannels/`

## `name`{% #name %}

**Type**: `STRING`**Provider name**: `name`**Description**: Output only. Resource name of a ModelDeploymentMonitoringJob.

## `next_schedule_time`{% #next_schedule_time %}

**Type**: `TIMESTAMP`**Provider name**: `nextScheduleTime`**Description**: Output only. Timestamp when this monitoring pipeline will be scheduled to run for the next round.

## `organization_id`{% #organization_id %}

**Type**: `STRING`

## `parent`{% #parent %}

**Type**: `STRING`

## `predict_instance_schema_uri`{% #predict_instance_schema_uri %}

**Type**: `STRING`**Provider name**: `predictInstanceSchemaUri`**Description**: YAML schema file uri describing the format of a single instance, which are given to format this Endpoint's prediction (and explanation). If not set, we will generate predict schema from collected predict requests.

## `project_id`{% #project_id %}

**Type**: `STRING`

## `project_number`{% #project_number %}

**Type**: `STRING`

## `region_id`{% #region_id %}

**Type**: `STRING`

## `resource_name`{% #resource_name %}

**Type**: `STRING`

## `satisfies_pzi`{% #satisfies_pzi %}

**Type**: `BOOLEAN`**Provider name**: `satisfiesPzi`**Description**: Output only. Reserved for future use.

## `satisfies_pzs`{% #satisfies_pzs %}

**Type**: `BOOLEAN`**Provider name**: `satisfiesPzs`**Description**: Output only. Reserved for future use.

## `schedule_state`{% #schedule_state %}

**Type**: `STRING`**Provider name**: `scheduleState`**Description**: Output only. Schedule state when the monitoring job is in Running state.**Possible values**:

- `MONITORING_SCHEDULE_STATE_UNSPECIFIED` - Unspecified state.
- `PENDING` - The pipeline is picked up and wait to run.
- `OFFLINE` - The pipeline is offline and will be scheduled for next run.
- `RUNNING` - The pipeline is running.

## `state`{% #state %}

**Type**: `STRING`**Provider name**: `state`**Description**: Output only. The detailed state of the monitoring job. When the job is still creating, the state will be 'PENDING'. Once the job is successfully created, the state will be 'RUNNING'. Pause the job, the state will be 'PAUSED'. Resume the job, the state will return to 'RUNNING'.**Possible values**:

- `JOB_STATE_UNSPECIFIED` - The job state is unspecified.
- `JOB_STATE_QUEUED` - The job has been just created or resumed and processing has not yet begun.
- `JOB_STATE_PENDING` - The service is preparing to run the job.
- `JOB_STATE_RUNNING` - The job is in progress.
- `JOB_STATE_SUCCEEDED` - The job completed successfully.
- `JOB_STATE_FAILED` - The job failed.
- `JOB_STATE_CANCELLING` - The job is being cancelled. From this state the job may only go to either `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.
- `JOB_STATE_CANCELLED` - The job has been cancelled.
- `JOB_STATE_PAUSED` - The job has been stopped, and can be resumed.
- `JOB_STATE_EXPIRED` - The job has expired.
- `JOB_STATE_UPDATING` - The job is being updated. Only jobs in the `RUNNING` state can be updated. After updating, the job goes back to the `RUNNING` state.
- `JOB_STATE_PARTIALLY_SUCCEEDED` - The job is partially succeeded, some results may be missing due to errors.

## `stats_anomalies_base_directory`{% #stats_anomalies_base_directory %}

**Type**: `STRUCT`**Provider name**: `statsAnomaliesBaseDirectory`**Description**: Stats anomalies base folder path.

- `output_uri_prefix`**Type**: `STRING`**Provider name**: `outputUriPrefix`**Description**: Required. Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.

## `tags`{% #tags %}

**Type**: `UNORDERED_LIST_STRING`

## `update_time`{% #update_time %}

**Type**: `TIMESTAMP`**Provider name**: `updateTime`**Description**: Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.

## `zone_id`{% #zone_id %}

**Type**: `STRING`