| _key | core | string | |
| analysis_instance_schema_uri | core | string | 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 | core | array<string> | |
| bigquery_tables | core | json | 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 |
| create_time | core | timestamp | Output only. Timestamp when this ModelDeploymentMonitoringJob was created. |
| datadog_display_name | core | string | |
| enable_monitoring_pipeline_logs | core | bool | 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 | core | json | 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. |
| endpoint | core | string | Required. Endpoint resource name. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` |
| error | core | json | Output only. Only populated when the job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`. |
| gcp_display_name | core | string | 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 | core | array<string> | |
| latest_monitoring_pipeline_metadata | core | json | Output only. Latest triggered monitoring pipeline metadata. |
| log_ttl | core | string | 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 | core | json | Required. Sample Strategy for logging. |
| model_deployment_monitoring_objective_configs | core | json | Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately. |
| model_deployment_monitoring_schedule_config | core | json | Required. Schedule config for running the monitoring job. |
| model_monitoring_alert_config | core | json | Alert config for model monitoring. |
| name | core | string | Output only. Resource name of a ModelDeploymentMonitoringJob. |
| next_schedule_time | core | timestamp | Output only. Timestamp when this monitoring pipeline will be scheduled to run for the next round. |
| organization_id | core | string | |
| parent | core | string | |
| predict_instance_schema_uri | core | string | 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 | core | string | |
| project_number | core | string | |
| resource_name | core | string | |
| satisfies_pzi | core | bool | Output only. Reserved for future use. |
| satisfies_pzs | core | bool | Output only. Reserved for future use. |
| schedule_state | core | string | Output only. Schedule state when the monitoring job is in Running state. Possible values: ['MONITORING_SCHEDULE_STATE_UNSPECIFIED', 'PENDING', 'OFFLINE', 'RUNNING']. Values descriptions: ['Unspecified state.', 'The pipeline is picked up and wait to run.', 'The pipeline is offline and will be scheduled for next run.', 'The pipeline is running.'] |
| state | core | string | 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', 'JOB_STATE_QUEUED', 'JOB_STATE_PENDING', 'JOB_STATE_RUNNING', 'JOB_STATE_SUCCEEDED', 'JOB_STATE_FAILED', 'JOB_STATE_CANCELLING', 'JOB_STATE_CANCELLED', 'JOB_STATE_PAUSED', 'JOB_STATE_EXPIRED', 'JOB_STATE_UPDATING', 'JOB_STATE_PARTIALLY_SUCCEEDED']. Values descriptions: ['The job state is unspecified.', 'The job has been just created or resumed and processing has not yet begun.', 'The service is preparing to run the job.', 'The job is in progress.', 'The job completed successfully.', 'The job failed.', '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`.', 'The job has been cancelled.', 'The job has been stopped, and can be resumed.', 'The job has expired.', 'The job is being updated. Only jobs in the `RUNNING` state can be updated. After updating, the job goes back to the `RUNNING` state.', 'The job is partially succeeded, some results may be missing due to errors.'] |
| stats_anomalies_base_directory | core | json | Stats anomalies base folder path. |
| tags | core | hstore | |
| update_time | core | timestamp | Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently. |
