Package com.google.cloud.aiplatform.v1beta1

Interface ModelDeploymentMonitoringJobOrBuilder

    • Method Detail

      • getName

        String getName()
         Output only. Resource name of a ModelDeploymentMonitoringJob.
        string name = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        The name.

      • getNameBytes

        com.google.protobuf.ByteString getNameBytes()
         Output only. Resource name of a ModelDeploymentMonitoringJob.
        string name = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        The bytes for name.

      • getDisplayName

        String getDisplayName()
         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.
        string display_name = 2 [(.google.api.field_behavior) = REQUIRED];
        Returns:
        The displayName.

      • getDisplayNameBytes

        com.google.protobuf.ByteString getDisplayNameBytes()
         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.
        string display_name = 2 [(.google.api.field_behavior) = REQUIRED];
        Returns:
        The bytes for displayName.

      • getEndpoint

        String getEndpoint()
         Required. Endpoint resource name.
 Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
        string endpoint = 3 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }
        Returns:
        The endpoint.

      • getEndpointBytes

        com.google.protobuf.ByteString getEndpointBytes()
         Required. Endpoint resource name.
 Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
        string endpoint = 3 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }
        Returns:
        The bytes for endpoint.

      • getStateValue

        int getStateValue()
         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'.
        .google.cloud.aiplatform.v1beta1.JobState state = 4 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        The enum numeric value on the wire for state.

      • getState

        JobState getState()
         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'.
        .google.cloud.aiplatform.v1beta1.JobState state = 4 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        The state.

      • getScheduleStateValue

        int getScheduleStateValue()
         Output only. Schedule state when the monitoring job is in Running state.
        .google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringJob.MonitoringScheduleState schedule_state = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        The enum numeric value on the wire for scheduleState.

      • getScheduleState

        ModelDeploymentMonitoringJob.MonitoringScheduleState getScheduleState()
         Output only. Schedule state when the monitoring job is in Running state.
        .google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringJob.MonitoringScheduleState schedule_state = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        The scheduleState.

      • hasLatestMonitoringPipelineMetadata

        boolean hasLatestMonitoringPipelineMetadata()
         Output only. Latest triggered monitoring pipeline metadata.
        .google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata latest_monitoring_pipeline_metadata = 25 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        Whether the latestMonitoringPipelineMetadata field is set.

      • getLatestMonitoringPipelineMetadata

        ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata getLatestMonitoringPipelineMetadata()
         Output only. Latest triggered monitoring pipeline metadata.
        .google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata latest_monitoring_pipeline_metadata = 25 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        The latestMonitoringPipelineMetadata.

      • getLatestMonitoringPipelineMetadataOrBuilder

        ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadataOrBuilder getLatestMonitoringPipelineMetadataOrBuilder()
         Output only. Latest triggered monitoring pipeline metadata.
        .google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata latest_monitoring_pipeline_metadata = 25 [(.google.api.field_behavior) = OUTPUT_ONLY];

      • getModelDeploymentMonitoringObjectiveConfigsList

        List<ModelDeploymentMonitoringObjectiveConfig> getModelDeploymentMonitoringObjectiveConfigsList()
         Required. The config for monitoring objectives. This is a per DeployedModel
 config. Each DeployedModel needs to be configured separately.
        repeated .google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];

      • getModelDeploymentMonitoringObjectiveConfigs

        ModelDeploymentMonitoringObjectiveConfig getModelDeploymentMonitoringObjectiveConfigs​(int index)
         Required. The config for monitoring objectives. This is a per DeployedModel
 config. Each DeployedModel needs to be configured separately.
        repeated .google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];

      • getModelDeploymentMonitoringObjectiveConfigsCount

        int getModelDeploymentMonitoringObjectiveConfigsCount()
         Required. The config for monitoring objectives. This is a per DeployedModel
 config. Each DeployedModel needs to be configured separately.
        repeated .google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];

      • getModelDeploymentMonitoringObjectiveConfigsOrBuilderList

        List<? extends ModelDeploymentMonitoringObjectiveConfigOrBuilder> getModelDeploymentMonitoringObjectiveConfigsOrBuilderList()
         Required. The config for monitoring objectives. This is a per DeployedModel
 config. Each DeployedModel needs to be configured separately.
        repeated .google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];

      • getModelDeploymentMonitoringObjectiveConfigsOrBuilder

        ModelDeploymentMonitoringObjectiveConfigOrBuilder getModelDeploymentMonitoringObjectiveConfigsOrBuilder​(int index)
         Required. The config for monitoring objectives. This is a per DeployedModel
 config. Each DeployedModel needs to be configured separately.
        repeated .google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];

      • hasModelDeploymentMonitoringScheduleConfig

        boolean hasModelDeploymentMonitoringScheduleConfig()
         Required. Schedule config for running the monitoring job.
        .google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringScheduleConfig model_deployment_monitoring_schedule_config = 7 [(.google.api.field_behavior) = REQUIRED];
        Returns:
        Whether the modelDeploymentMonitoringScheduleConfig field is set.

      • getModelDeploymentMonitoringScheduleConfig

        ModelDeploymentMonitoringScheduleConfig getModelDeploymentMonitoringScheduleConfig()
         Required. Schedule config for running the monitoring job.
        .google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringScheduleConfig model_deployment_monitoring_schedule_config = 7 [(.google.api.field_behavior) = REQUIRED];
        Returns:
        The modelDeploymentMonitoringScheduleConfig.

      • getModelDeploymentMonitoringScheduleConfigOrBuilder

        ModelDeploymentMonitoringScheduleConfigOrBuilder getModelDeploymentMonitoringScheduleConfigOrBuilder()
         Required. Schedule config for running the monitoring job.
        .google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringScheduleConfig model_deployment_monitoring_schedule_config = 7 [(.google.api.field_behavior) = REQUIRED];

      • hasLoggingSamplingStrategy

        boolean hasLoggingSamplingStrategy()
         Required. Sample Strategy for logging.
        .google.cloud.aiplatform.v1beta1.SamplingStrategy logging_sampling_strategy = 8 [(.google.api.field_behavior) = REQUIRED];
        Returns:
        Whether the loggingSamplingStrategy field is set.

      • getLoggingSamplingStrategy

        SamplingStrategy getLoggingSamplingStrategy()
         Required. Sample Strategy for logging.
        .google.cloud.aiplatform.v1beta1.SamplingStrategy logging_sampling_strategy = 8 [(.google.api.field_behavior) = REQUIRED];
        Returns:
        The loggingSamplingStrategy.

      • getLoggingSamplingStrategyOrBuilder

        SamplingStrategyOrBuilder getLoggingSamplingStrategyOrBuilder()
         Required. Sample Strategy for logging.
        .google.cloud.aiplatform.v1beta1.SamplingStrategy logging_sampling_strategy = 8 [(.google.api.field_behavior) = REQUIRED];

      • hasModelMonitoringAlertConfig

        boolean hasModelMonitoringAlertConfig()
         Alert config for model monitoring.
        .google.cloud.aiplatform.v1beta1.ModelMonitoringAlertConfig model_monitoring_alert_config = 15;
        Returns:
        Whether the modelMonitoringAlertConfig field is set.

      • getModelMonitoringAlertConfig

        ModelMonitoringAlertConfig getModelMonitoringAlertConfig()
         Alert config for model monitoring.
        .google.cloud.aiplatform.v1beta1.ModelMonitoringAlertConfig model_monitoring_alert_config = 15;
        Returns:
        The modelMonitoringAlertConfig.

      • getModelMonitoringAlertConfigOrBuilder

        ModelMonitoringAlertConfigOrBuilder getModelMonitoringAlertConfigOrBuilder()
         Alert config for model monitoring.
        .google.cloud.aiplatform.v1beta1.ModelMonitoringAlertConfig model_monitoring_alert_config = 15;

      • getPredictInstanceSchemaUri

        String getPredictInstanceSchemaUri()
         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.
        string predict_instance_schema_uri = 9;
        Returns:
        The predictInstanceSchemaUri.

      • getPredictInstanceSchemaUriBytes

        com.google.protobuf.ByteString getPredictInstanceSchemaUriBytes()
         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.
        string predict_instance_schema_uri = 9;
        Returns:
        The bytes for predictInstanceSchemaUri.

      • hasSamplePredictInstance

        boolean hasSamplePredictInstance()
         Sample Predict instance, same format as
 [PredictRequest.instances][google.cloud.aiplatform.v1beta1.PredictRequest.instances],
 this can be set as a replacement of
 [ModelDeploymentMonitoringJob.predict_instance_schema_uri][google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringJob.predict_instance_schema_uri].
 If not set, we will generate predict schema from collected predict
 requests.
        .google.protobuf.Value sample_predict_instance = 19;
        Returns:
        Whether the samplePredictInstance field is set.

      • getSamplePredictInstance

        com.google.protobuf.Value getSamplePredictInstance()
         Sample Predict instance, same format as
 [PredictRequest.instances][google.cloud.aiplatform.v1beta1.PredictRequest.instances],
 this can be set as a replacement of
 [ModelDeploymentMonitoringJob.predict_instance_schema_uri][google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringJob.predict_instance_schema_uri].
 If not set, we will generate predict schema from collected predict
 requests.
        .google.protobuf.Value sample_predict_instance = 19;
        Returns:
        The samplePredictInstance.

      • getSamplePredictInstanceOrBuilder

        com.google.protobuf.ValueOrBuilder getSamplePredictInstanceOrBuilder()
         Sample Predict instance, same format as
 [PredictRequest.instances][google.cloud.aiplatform.v1beta1.PredictRequest.instances],
 this can be set as a replacement of
 [ModelDeploymentMonitoringJob.predict_instance_schema_uri][google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringJob.predict_instance_schema_uri].
 If not set, we will generate predict schema from collected predict
 requests.
        .google.protobuf.Value sample_predict_instance = 19;

      • getAnalysisInstanceSchemaUri

        String getAnalysisInstanceSchemaUri()
         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][google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringJob.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.
        string analysis_instance_schema_uri = 16;
        Returns:
        The analysisInstanceSchemaUri.

      • getAnalysisInstanceSchemaUriBytes

        com.google.protobuf.ByteString getAnalysisInstanceSchemaUriBytes()
         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][google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringJob.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.
        string analysis_instance_schema_uri = 16;
        Returns:
        The bytes for analysisInstanceSchemaUri.

      • getBigqueryTablesList

        List<ModelDeploymentMonitoringBigQueryTable> getBigqueryTablesList()
         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
        repeated .google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

      • getBigqueryTables

        ModelDeploymentMonitoringBigQueryTable getBigqueryTables​(int index)
         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
        repeated .google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

      • getBigqueryTablesCount

        int getBigqueryTablesCount()
         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
        repeated .google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

      • getBigqueryTablesOrBuilderList

        List<? extends ModelDeploymentMonitoringBigQueryTableOrBuilder> getBigqueryTablesOrBuilderList()
         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
        repeated .google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

      • getBigqueryTablesOrBuilder

        ModelDeploymentMonitoringBigQueryTableOrBuilder getBigqueryTablesOrBuilder​(int index)
         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
        repeated .google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

      • hasLogTtl

        boolean hasLogTtl()
         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.
        .google.protobuf.Duration log_ttl = 17;
        Returns:
        Whether the logTtl field is set.

      • getLogTtl

        com.google.protobuf.Duration getLogTtl()
         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.
        .google.protobuf.Duration log_ttl = 17;
        Returns:
        The logTtl.

      • getLogTtlOrBuilder

        com.google.protobuf.DurationOrBuilder getLogTtlOrBuilder()
         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.
        .google.protobuf.Duration log_ttl = 17;

      • getLabelsCount

        int getLabelsCount()
         The labels with user-defined metadata to organize your
 ModelDeploymentMonitoringJob.

 Label keys and values can be no longer than 64 characters
 (Unicode codepoints), can only contain lowercase letters, numeric
 characters, underscores and dashes. International characters are allowed.

 See https://goo.gl/xmQnxf for more information and examples of labels.
        map<string, string> labels = 11;

      • containsLabels

        boolean containsLabels​(String key)
         The labels with user-defined metadata to organize your
 ModelDeploymentMonitoringJob.

 Label keys and values can be no longer than 64 characters
 (Unicode codepoints), can only contain lowercase letters, numeric
 characters, underscores and dashes. International characters are allowed.

 See https://goo.gl/xmQnxf for more information and examples of labels.
        map<string, string> labels = 11;

      • getLabelsMap

        Map<String,​String> getLabelsMap()
         The labels with user-defined metadata to organize your
 ModelDeploymentMonitoringJob.

 Label keys and values can be no longer than 64 characters
 (Unicode codepoints), can only contain lowercase letters, numeric
 characters, underscores and dashes. International characters are allowed.

 See https://goo.gl/xmQnxf for more information and examples of labels.
        map<string, string> labels = 11;

      • getLabelsOrDefault

        String getLabelsOrDefault​(String key,
                          String defaultValue)
         The labels with user-defined metadata to organize your
 ModelDeploymentMonitoringJob.

 Label keys and values can be no longer than 64 characters
 (Unicode codepoints), can only contain lowercase letters, numeric
 characters, underscores and dashes. International characters are allowed.

 See https://goo.gl/xmQnxf for more information and examples of labels.
        map<string, string> labels = 11;

      • getLabelsOrThrow

        String getLabelsOrThrow​(String key)
         The labels with user-defined metadata to organize your
 ModelDeploymentMonitoringJob.

 Label keys and values can be no longer than 64 characters
 (Unicode codepoints), can only contain lowercase letters, numeric
 characters, underscores and dashes. International characters are allowed.

 See https://goo.gl/xmQnxf for more information and examples of labels.
        map<string, string> labels = 11;

      • hasCreateTime

        boolean hasCreateTime()
         Output only. Timestamp when this ModelDeploymentMonitoringJob was created.
        .google.protobuf.Timestamp create_time = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        Whether the createTime field is set.

      • getCreateTime

        com.google.protobuf.Timestamp getCreateTime()
         Output only. Timestamp when this ModelDeploymentMonitoringJob was created.
        .google.protobuf.Timestamp create_time = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        The createTime.

      • getCreateTimeOrBuilder

        com.google.protobuf.TimestampOrBuilder getCreateTimeOrBuilder()
         Output only. Timestamp when this ModelDeploymentMonitoringJob was created.
        .google.protobuf.Timestamp create_time = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];

      • hasUpdateTime

        boolean hasUpdateTime()
         Output only. Timestamp when this ModelDeploymentMonitoringJob was updated
 most recently.
        .google.protobuf.Timestamp update_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        Whether the updateTime field is set.

      • getUpdateTime

        com.google.protobuf.Timestamp getUpdateTime()
         Output only. Timestamp when this ModelDeploymentMonitoringJob was updated
 most recently.
        .google.protobuf.Timestamp update_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        The updateTime.

      • getUpdateTimeOrBuilder

        com.google.protobuf.TimestampOrBuilder getUpdateTimeOrBuilder()
         Output only. Timestamp when this ModelDeploymentMonitoringJob was updated
 most recently.
        .google.protobuf.Timestamp update_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];

      • hasNextScheduleTime

        boolean hasNextScheduleTime()
         Output only. Timestamp when this monitoring pipeline will be scheduled to
 run for the next round.
        .google.protobuf.Timestamp next_schedule_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        Whether the nextScheduleTime field is set.

      • getNextScheduleTime

        com.google.protobuf.Timestamp getNextScheduleTime()
         Output only. Timestamp when this monitoring pipeline will be scheduled to
 run for the next round.
        .google.protobuf.Timestamp next_schedule_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        The nextScheduleTime.

      • getNextScheduleTimeOrBuilder

        com.google.protobuf.TimestampOrBuilder getNextScheduleTimeOrBuilder()
         Output only. Timestamp when this monitoring pipeline will be scheduled to
 run for the next round.
        .google.protobuf.Timestamp next_schedule_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

      • hasStatsAnomaliesBaseDirectory

        boolean hasStatsAnomaliesBaseDirectory()
         Stats anomalies base folder path.
        .google.cloud.aiplatform.v1beta1.GcsDestination stats_anomalies_base_directory = 20;
        Returns:
        Whether the statsAnomaliesBaseDirectory field is set.

      • getStatsAnomaliesBaseDirectory

        GcsDestination getStatsAnomaliesBaseDirectory()
         Stats anomalies base folder path.
        .google.cloud.aiplatform.v1beta1.GcsDestination stats_anomalies_base_directory = 20;
        Returns:
        The statsAnomaliesBaseDirectory.

      • getStatsAnomaliesBaseDirectoryOrBuilder

        GcsDestinationOrBuilder getStatsAnomaliesBaseDirectoryOrBuilder()
         Stats anomalies base folder path.
        .google.cloud.aiplatform.v1beta1.GcsDestination stats_anomalies_base_directory = 20;

      • hasEncryptionSpec

        boolean hasEncryptionSpec()
         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.
        .google.cloud.aiplatform.v1beta1.EncryptionSpec encryption_spec = 21;
        Returns:
        Whether the encryptionSpec field is set.

      • getEncryptionSpec

        EncryptionSpec getEncryptionSpec()
         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.
        .google.cloud.aiplatform.v1beta1.EncryptionSpec encryption_spec = 21;
        Returns:
        The encryptionSpec.

      • getEncryptionSpecOrBuilder

        EncryptionSpecOrBuilder getEncryptionSpecOrBuilder()
         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.
        .google.cloud.aiplatform.v1beta1.EncryptionSpec encryption_spec = 21;

      • getEnableMonitoringPipelineLogs

        boolean getEnableMonitoringPipelineLogs()
         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).
        bool enable_monitoring_pipeline_logs = 22;
        Returns:
        The enableMonitoringPipelineLogs.

      • hasError

        boolean hasError()
         Output only. Only populated when the job's state is `JOB_STATE_FAILED` or
 `JOB_STATE_CANCELLED`.
        .google.rpc.Status error = 23 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        Whether the error field is set.

      • getError

        com.google.rpc.Status getError()
         Output only. Only populated when the job's state is `JOB_STATE_FAILED` or
 `JOB_STATE_CANCELLED`.
        .google.rpc.Status error = 23 [(.google.api.field_behavior) = OUTPUT_ONLY];
        Returns:
        The error.

      • getErrorOrBuilder

        com.google.rpc.StatusOrBuilder getErrorOrBuilder()
         Output only. Only populated when the job's state is `JOB_STATE_FAILED` or
 `JOB_STATE_CANCELLED`.
        .google.rpc.Status error = 23 [(.google.api.field_behavior) = OUTPUT_ONLY];