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public static final class ModelDeploymentMonitoringJob.Builder extends GeneratedMessageV3.Builder<ModelDeploymentMonitoringJob.Builder> implements ModelDeploymentMonitoringJobOrBuilderRepresents a job that runs periodically to monitor the deployed models in an endpoint. It will analyze the logged training & prediction data to detect any abnormal behaviors.
Protobuf type google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob
Inheritance
Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > ModelDeploymentMonitoringJob.BuilderImplements
ModelDeploymentMonitoringJobOrBuilderMethods
addAllBigqueryTables(Iterable<? extends ModelDeploymentMonitoringBigQueryTable> values)
public ModelDeploymentMonitoringJob.Builder addAllBigqueryTables(Iterable<? extends ModelDeploymentMonitoringBigQueryTable> values)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:
- Training data logging predict request/response
- Serving data logging predict request/response
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| values | Iterable<? extends com.google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable> |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
addAllModelDeploymentMonitoringObjectiveConfigs(Iterable<? extends ModelDeploymentMonitoringObjectiveConfig> values)
public ModelDeploymentMonitoringJob.Builder addAllModelDeploymentMonitoringObjectiveConfigs(Iterable<? extends ModelDeploymentMonitoringObjectiveConfig> values)Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| values | Iterable<? extends com.google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig> |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
addBigqueryTables(ModelDeploymentMonitoringBigQueryTable value)
public ModelDeploymentMonitoringJob.Builder addBigqueryTables(ModelDeploymentMonitoringBigQueryTable value)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:
- Training data logging predict request/response
- Serving data logging predict request/response
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| value | ModelDeploymentMonitoringBigQueryTable |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
addBigqueryTables(ModelDeploymentMonitoringBigQueryTable.Builder builderForValue)
public ModelDeploymentMonitoringJob.Builder addBigqueryTables(ModelDeploymentMonitoringBigQueryTable.Builder builderForValue)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:
- Training data logging predict request/response
- Serving data logging predict request/response
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| builderForValue | ModelDeploymentMonitoringBigQueryTable.Builder |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
addBigqueryTables(int index, ModelDeploymentMonitoringBigQueryTable value)
public ModelDeploymentMonitoringJob.Builder addBigqueryTables(int index, ModelDeploymentMonitoringBigQueryTable value)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:
- Training data logging predict request/response
- Serving data logging predict request/response
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| index | int |
| value | ModelDeploymentMonitoringBigQueryTable |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
addBigqueryTables(int index, ModelDeploymentMonitoringBigQueryTable.Builder builderForValue)
public ModelDeploymentMonitoringJob.Builder addBigqueryTables(int index, ModelDeploymentMonitoringBigQueryTable.Builder builderForValue)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:
- Training data logging predict request/response
- Serving data logging predict request/response
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| index | int |
| builderForValue | ModelDeploymentMonitoringBigQueryTable.Builder |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
addBigqueryTablesBuilder()
public ModelDeploymentMonitoringBigQueryTable.Builder addBigqueryTablesBuilder()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:
- Training data logging predict request/response
- Serving data logging predict request/response
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| ModelDeploymentMonitoringBigQueryTable.Builder |
addBigqueryTablesBuilder(int index)
public ModelDeploymentMonitoringBigQueryTable.Builder addBigqueryTablesBuilder(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:
- Training data logging predict request/response
- Serving data logging predict request/response
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| index | int |
| Type | Description |
| ModelDeploymentMonitoringBigQueryTable.Builder |
addModelDeploymentMonitoringObjectiveConfigs(ModelDeploymentMonitoringObjectiveConfig value)
public ModelDeploymentMonitoringJob.Builder addModelDeploymentMonitoringObjectiveConfigs(ModelDeploymentMonitoringObjectiveConfig value)Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| value | ModelDeploymentMonitoringObjectiveConfig |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
addModelDeploymentMonitoringObjectiveConfigs(ModelDeploymentMonitoringObjectiveConfig.Builder builderForValue)
public ModelDeploymentMonitoringJob.Builder addModelDeploymentMonitoringObjectiveConfigs(ModelDeploymentMonitoringObjectiveConfig.Builder builderForValue)Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| builderForValue | ModelDeploymentMonitoringObjectiveConfig.Builder |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
addModelDeploymentMonitoringObjectiveConfigs(int index, ModelDeploymentMonitoringObjectiveConfig value)
public ModelDeploymentMonitoringJob.Builder addModelDeploymentMonitoringObjectiveConfigs(int index, ModelDeploymentMonitoringObjectiveConfig value)Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| index | int |
| value | ModelDeploymentMonitoringObjectiveConfig |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
addModelDeploymentMonitoringObjectiveConfigs(int index, ModelDeploymentMonitoringObjectiveConfig.Builder builderForValue)
public ModelDeploymentMonitoringJob.Builder addModelDeploymentMonitoringObjectiveConfigs(int index, ModelDeploymentMonitoringObjectiveConfig.Builder builderForValue)Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| index | int |
| builderForValue | ModelDeploymentMonitoringObjectiveConfig.Builder |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
addModelDeploymentMonitoringObjectiveConfigsBuilder()
public ModelDeploymentMonitoringObjectiveConfig.Builder addModelDeploymentMonitoringObjectiveConfigsBuilder()Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| ModelDeploymentMonitoringObjectiveConfig.Builder |
addModelDeploymentMonitoringObjectiveConfigsBuilder(int index)
public ModelDeploymentMonitoringObjectiveConfig.Builder addModelDeploymentMonitoringObjectiveConfigsBuilder(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.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| index | int |
| Type | Description |
| ModelDeploymentMonitoringObjectiveConfig.Builder |
addRepeatedField(Descriptors.FieldDescriptor field, Object value)
public ModelDeploymentMonitoringJob.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)| Name | Description |
| field | FieldDescriptor |
| value | Object |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
build()
public ModelDeploymentMonitoringJob build()| Type | Description |
| ModelDeploymentMonitoringJob |
buildPartial()
public ModelDeploymentMonitoringJob buildPartial()| Type | Description |
| ModelDeploymentMonitoringJob |
clear()
public ModelDeploymentMonitoringJob.Builder clear()| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
clearAnalysisInstanceSchemaUri()
public ModelDeploymentMonitoringJob.Builder clearAnalysisInstanceSchemaUri()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.
string analysis_instance_schema_uri = 16;
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
clearBigqueryTables()
public ModelDeploymentMonitoringJob.Builder clearBigqueryTables()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:
- Training data logging predict request/response
- Serving data logging predict request/response
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
clearCreateTime()
public ModelDeploymentMonitoringJob.Builder clearCreateTime()Output only. Timestamp when this ModelDeploymentMonitoringJob was created.
.google.protobuf.Timestamp create_time = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
clearDisplayName()
public ModelDeploymentMonitoringJob.Builder clearDisplayName()Required. The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.
string display_name = 2 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
clearEnableMonitoringPipelineLogs()
public ModelDeploymentMonitoringJob.Builder clearEnableMonitoringPipelineLogs()If true, the scheduled monitoring pipeline status logs are sent to Google Cloud Logging. Please note the logs incur cost, which are subject to Cloud Logging pricing.
bool enable_monitoring_pipeline_logs = 22;
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
clearEncryptionSpec()
public ModelDeploymentMonitoringJob.Builder clearEncryptionSpec()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.v1.EncryptionSpec encryption_spec = 21;
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
clearEndpoint()
public ModelDeploymentMonitoringJob.Builder clearEndpoint() Required. Endpoint resource name.
Format: projects/{project}/locations/{location}/endpoints/{endpoint}
string endpoint = 3 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
clearError()
public ModelDeploymentMonitoringJob.Builder clearError() 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];
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
clearField(Descriptors.FieldDescriptor field)
public ModelDeploymentMonitoringJob.Builder clearField(Descriptors.FieldDescriptor field)| Name | Description |
| field | FieldDescriptor |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
clearLabels()
public ModelDeploymentMonitoringJob.Builder clearLabels()| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
clearLogTtl()
public ModelDeploymentMonitoringJob.Builder clearLogTtl()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;
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
clearLoggingSamplingStrategy()
public ModelDeploymentMonitoringJob.Builder clearLoggingSamplingStrategy()Required. Sample Strategy for logging.
.google.cloud.aiplatform.v1.SamplingStrategy logging_sampling_strategy = 8 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
clearModelDeploymentMonitoringObjectiveConfigs()
public ModelDeploymentMonitoringJob.Builder clearModelDeploymentMonitoringObjectiveConfigs()Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
clearModelDeploymentMonitoringScheduleConfig()
public ModelDeploymentMonitoringJob.Builder clearModelDeploymentMonitoringScheduleConfig()Required. Schedule config for running the monitoring job.
.google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig model_deployment_monitoring_schedule_config = 7 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
clearModelMonitoringAlertConfig()
public ModelDeploymentMonitoringJob.Builder clearModelMonitoringAlertConfig()Alert config for model monitoring.
.google.cloud.aiplatform.v1.ModelMonitoringAlertConfig model_monitoring_alert_config = 15;
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
clearName()
public ModelDeploymentMonitoringJob.Builder clearName()Output only. Resource name of a ModelDeploymentMonitoringJob.
string name = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
clearNextScheduleTime()
public ModelDeploymentMonitoringJob.Builder clearNextScheduleTime()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];
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
clearOneof(Descriptors.OneofDescriptor oneof)
public ModelDeploymentMonitoringJob.Builder clearOneof(Descriptors.OneofDescriptor oneof)| Name | Description |
| oneof | OneofDescriptor |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
clearPredictInstanceSchemaUri()
public ModelDeploymentMonitoringJob.Builder clearPredictInstanceSchemaUri()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;
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
clearSamplePredictInstance()
public ModelDeploymentMonitoringJob.Builder clearSamplePredictInstance()Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests.
.google.protobuf.Value sample_predict_instance = 19;
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
clearScheduleState()
public ModelDeploymentMonitoringJob.Builder clearScheduleState()Output only. Schedule state when the monitoring job is in Running state.
.google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.MonitoringScheduleState schedule_state = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
clearState()
public ModelDeploymentMonitoringJob.Builder clearState()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.v1.JobState state = 4 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
clearStatsAnomaliesBaseDirectory()
public ModelDeploymentMonitoringJob.Builder clearStatsAnomaliesBaseDirectory()Stats anomalies base folder path.
.google.cloud.aiplatform.v1.GcsDestination stats_anomalies_base_directory = 20;
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
clearUpdateTime()
public ModelDeploymentMonitoringJob.Builder clearUpdateTime()Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
.google.protobuf.Timestamp update_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
clone()
public ModelDeploymentMonitoringJob.Builder clone()| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
containsLabels(String key)
public 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;
| Name | Description |
| key | String |
| Type | Description |
| boolean |
getAnalysisInstanceSchemaUri()
public 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, 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;
| Type | Description |
| String | The analysisInstanceSchemaUri. |
getAnalysisInstanceSchemaUriBytes()
public 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, 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;
| Type | Description |
| ByteString | The bytes for analysisInstanceSchemaUri. |
getBigqueryTables(int index)
public 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:
- Training data logging predict request/response
- Serving data logging predict request/response
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| index | int |
| Type | Description |
| ModelDeploymentMonitoringBigQueryTable |
getBigqueryTablesBuilder(int index)
public ModelDeploymentMonitoringBigQueryTable.Builder getBigqueryTablesBuilder(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:
- Training data logging predict request/response
- Serving data logging predict request/response
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| index | int |
| Type | Description |
| ModelDeploymentMonitoringBigQueryTable.Builder |
getBigqueryTablesBuilderList()
public List<ModelDeploymentMonitoringBigQueryTable.Builder> getBigqueryTablesBuilderList()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:
- Training data logging predict request/response
- Serving data logging predict request/response
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| List<Builder> |
getBigqueryTablesCount()
public 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:
- Training data logging predict request/response
- Serving data logging predict request/response
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| int |
getBigqueryTablesList()
public 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:
- Training data logging predict request/response
- Serving data logging predict request/response
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| List<ModelDeploymentMonitoringBigQueryTable> |
getBigqueryTablesOrBuilder(int index)
public 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:
- Training data logging predict request/response
- Serving data logging predict request/response
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| index | int |
| Type | Description |
| ModelDeploymentMonitoringBigQueryTableOrBuilder |
getBigqueryTablesOrBuilderList()
public 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:
- Training data logging predict request/response
- Serving data logging predict request/response
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| List<? extends com.google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTableOrBuilder> |
getCreateTime()
public Timestamp getCreateTime()Output only. Timestamp when this ModelDeploymentMonitoringJob was created.
.google.protobuf.Timestamp create_time = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| Timestamp | The createTime. |
getCreateTimeBuilder()
public Timestamp.Builder getCreateTimeBuilder()Output only. Timestamp when this ModelDeploymentMonitoringJob was created.
.google.protobuf.Timestamp create_time = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| Builder |
getCreateTimeOrBuilder()
public TimestampOrBuilder getCreateTimeOrBuilder()Output only. Timestamp when this ModelDeploymentMonitoringJob was created.
.google.protobuf.Timestamp create_time = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| TimestampOrBuilder |
getDefaultInstanceForType()
public ModelDeploymentMonitoringJob getDefaultInstanceForType()| Type | Description |
| ModelDeploymentMonitoringJob |
getDescriptor()
public static final Descriptors.Descriptor getDescriptor()| Type | Description |
| Descriptor |
getDescriptorForType()
public Descriptors.Descriptor getDescriptorForType()| Type | Description |
| Descriptor |
getDisplayName()
public String getDisplayName()Required. The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.
string display_name = 2 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| String | The displayName. |
getDisplayNameBytes()
public ByteString getDisplayNameBytes()Required. The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.
string display_name = 2 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| ByteString | The bytes for displayName. |
getEnableMonitoringPipelineLogs()
public boolean getEnableMonitoringPipelineLogs()If true, the scheduled monitoring pipeline status logs are sent to Google Cloud Logging. Please note the logs incur cost, which are subject to Cloud Logging pricing.
bool enable_monitoring_pipeline_logs = 22;
| Type | Description |
| boolean | The enableMonitoringPipelineLogs. |
getEncryptionSpec()
public 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.v1.EncryptionSpec encryption_spec = 21;
| Type | Description |
| EncryptionSpec | The encryptionSpec. |
getEncryptionSpecBuilder()
public EncryptionSpec.Builder getEncryptionSpecBuilder()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.v1.EncryptionSpec encryption_spec = 21;
| Type | Description |
| EncryptionSpec.Builder |
getEncryptionSpecOrBuilder()
public 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.v1.EncryptionSpec encryption_spec = 21;
| Type | Description |
| EncryptionSpecOrBuilder |
getEndpoint()
public 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) = { ... }
| Type | Description |
| String | The endpoint. |
getEndpointBytes()
public 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) = { ... }
| Type | Description |
| ByteString | The bytes for endpoint. |
getError()
public 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];
| Type | Description |
| com.google.rpc.Status | The error. |
getErrorBuilder()
public Status.Builder getErrorBuilder() 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];
| Type | Description |
| com.google.rpc.Status.Builder |
getErrorOrBuilder()
public 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];
| Type | Description |
| com.google.rpc.StatusOrBuilder |
getLabels()
public Map<String,String> getLabels()Use #getLabelsMap() instead.
| Type | Description |
| Map<String,String> |
getLabelsCount()
public 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;
| Type | Description |
| int |
getLabelsMap()
public 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;
| Type | Description |
| Map<String,String> |
getLabelsOrDefault(String key, String defaultValue)
public 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;
| Name | Description |
| key | String |
| defaultValue | String |
| Type | Description |
| String |
getLabelsOrThrow(String key)
public 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;
| Name | Description |
| key | String |
| Type | Description |
| String |
getLogTtl()
public 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;
| Type | Description |
| Duration | The logTtl. |
getLogTtlBuilder()
public Duration.Builder getLogTtlBuilder()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;
| Type | Description |
| Builder |
getLogTtlOrBuilder()
public 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;
| Type | Description |
| DurationOrBuilder |
getLoggingSamplingStrategy()
public SamplingStrategy getLoggingSamplingStrategy()Required. Sample Strategy for logging.
.google.cloud.aiplatform.v1.SamplingStrategy logging_sampling_strategy = 8 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| SamplingStrategy | The loggingSamplingStrategy. |
getLoggingSamplingStrategyBuilder()
public SamplingStrategy.Builder getLoggingSamplingStrategyBuilder()Required. Sample Strategy for logging.
.google.cloud.aiplatform.v1.SamplingStrategy logging_sampling_strategy = 8 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| SamplingStrategy.Builder |
getLoggingSamplingStrategyOrBuilder()
public SamplingStrategyOrBuilder getLoggingSamplingStrategyOrBuilder()Required. Sample Strategy for logging.
.google.cloud.aiplatform.v1.SamplingStrategy logging_sampling_strategy = 8 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| SamplingStrategyOrBuilder |
getModelDeploymentMonitoringObjectiveConfigs(int index)
public 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.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| index | int |
| Type | Description |
| ModelDeploymentMonitoringObjectiveConfig |
getModelDeploymentMonitoringObjectiveConfigsBuilder(int index)
public ModelDeploymentMonitoringObjectiveConfig.Builder getModelDeploymentMonitoringObjectiveConfigsBuilder(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.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| index | int |
| Type | Description |
| ModelDeploymentMonitoringObjectiveConfig.Builder |
getModelDeploymentMonitoringObjectiveConfigsBuilderList()
public List<ModelDeploymentMonitoringObjectiveConfig.Builder> getModelDeploymentMonitoringObjectiveConfigsBuilderList()Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| List<Builder> |
getModelDeploymentMonitoringObjectiveConfigsCount()
public 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.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| int |
getModelDeploymentMonitoringObjectiveConfigsList()
public 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.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| List<ModelDeploymentMonitoringObjectiveConfig> |
getModelDeploymentMonitoringObjectiveConfigsOrBuilder(int index)
public 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.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| index | int |
| Type | Description |
| ModelDeploymentMonitoringObjectiveConfigOrBuilder |
getModelDeploymentMonitoringObjectiveConfigsOrBuilderList()
public 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.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| List<? extends com.google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfigOrBuilder> |
getModelDeploymentMonitoringScheduleConfig()
public ModelDeploymentMonitoringScheduleConfig getModelDeploymentMonitoringScheduleConfig()Required. Schedule config for running the monitoring job.
.google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig model_deployment_monitoring_schedule_config = 7 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| ModelDeploymentMonitoringScheduleConfig | The modelDeploymentMonitoringScheduleConfig. |
getModelDeploymentMonitoringScheduleConfigBuilder()
public ModelDeploymentMonitoringScheduleConfig.Builder getModelDeploymentMonitoringScheduleConfigBuilder()Required. Schedule config for running the monitoring job.
.google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig model_deployment_monitoring_schedule_config = 7 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| ModelDeploymentMonitoringScheduleConfig.Builder |
getModelDeploymentMonitoringScheduleConfigOrBuilder()
public ModelDeploymentMonitoringScheduleConfigOrBuilder getModelDeploymentMonitoringScheduleConfigOrBuilder()Required. Schedule config for running the monitoring job.
.google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig model_deployment_monitoring_schedule_config = 7 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| ModelDeploymentMonitoringScheduleConfigOrBuilder |
getModelMonitoringAlertConfig()
public ModelMonitoringAlertConfig getModelMonitoringAlertConfig()Alert config for model monitoring.
.google.cloud.aiplatform.v1.ModelMonitoringAlertConfig model_monitoring_alert_config = 15;
| Type | Description |
| ModelMonitoringAlertConfig | The modelMonitoringAlertConfig. |
getModelMonitoringAlertConfigBuilder()
public ModelMonitoringAlertConfig.Builder getModelMonitoringAlertConfigBuilder()Alert config for model monitoring.
.google.cloud.aiplatform.v1.ModelMonitoringAlertConfig model_monitoring_alert_config = 15;
| Type | Description |
| ModelMonitoringAlertConfig.Builder |
getModelMonitoringAlertConfigOrBuilder()
public ModelMonitoringAlertConfigOrBuilder getModelMonitoringAlertConfigOrBuilder()Alert config for model monitoring.
.google.cloud.aiplatform.v1.ModelMonitoringAlertConfig model_monitoring_alert_config = 15;
| Type | Description |
| ModelMonitoringAlertConfigOrBuilder |
getMutableLabels()
public Map<String,String> getMutableLabels()Use alternate mutation accessors instead.
| Type | Description |
| Map<String,String> |
getName()
public String getName()Output only. Resource name of a ModelDeploymentMonitoringJob.
string name = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| String | The name. |
getNameBytes()
public ByteString getNameBytes()Output only. Resource name of a ModelDeploymentMonitoringJob.
string name = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| ByteString | The bytes for name. |
getNextScheduleTime()
public 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];
| Type | Description |
| Timestamp | The nextScheduleTime. |
getNextScheduleTimeBuilder()
public Timestamp.Builder getNextScheduleTimeBuilder()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];
| Type | Description |
| Builder |
getNextScheduleTimeOrBuilder()
public 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];
| Type | Description |
| TimestampOrBuilder |
getPredictInstanceSchemaUri()
public 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;
| Type | Description |
| String | The predictInstanceSchemaUri. |
getPredictInstanceSchemaUriBytes()
public 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;
| Type | Description |
| ByteString | The bytes for predictInstanceSchemaUri. |
getSamplePredictInstance()
public Value getSamplePredictInstance()Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests.
.google.protobuf.Value sample_predict_instance = 19;
| Type | Description |
| Value | The samplePredictInstance. |
getSamplePredictInstanceBuilder()
public Value.Builder getSamplePredictInstanceBuilder()Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests.
.google.protobuf.Value sample_predict_instance = 19;
| Type | Description |
| Builder |
getSamplePredictInstanceOrBuilder()
public ValueOrBuilder getSamplePredictInstanceOrBuilder()Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests.
.google.protobuf.Value sample_predict_instance = 19;
| Type | Description |
| ValueOrBuilder |
getScheduleState()
public ModelDeploymentMonitoringJob.MonitoringScheduleState getScheduleState()Output only. Schedule state when the monitoring job is in Running state.
.google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.MonitoringScheduleState schedule_state = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| ModelDeploymentMonitoringJob.MonitoringScheduleState | The scheduleState. |
getScheduleStateValue()
public int getScheduleStateValue()Output only. Schedule state when the monitoring job is in Running state.
.google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.MonitoringScheduleState schedule_state = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| int | The enum numeric value on the wire for scheduleState. |
getState()
public 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.v1.JobState state = 4 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| JobState | The state. |
getStateValue()
public 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.v1.JobState state = 4 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| int | The enum numeric value on the wire for state. |
getStatsAnomaliesBaseDirectory()
public GcsDestination getStatsAnomaliesBaseDirectory()Stats anomalies base folder path.
.google.cloud.aiplatform.v1.GcsDestination stats_anomalies_base_directory = 20;
| Type | Description |
| GcsDestination | The statsAnomaliesBaseDirectory. |
getStatsAnomaliesBaseDirectoryBuilder()
public GcsDestination.Builder getStatsAnomaliesBaseDirectoryBuilder()Stats anomalies base folder path.
.google.cloud.aiplatform.v1.GcsDestination stats_anomalies_base_directory = 20;
| Type | Description |
| GcsDestination.Builder |
getStatsAnomaliesBaseDirectoryOrBuilder()
public GcsDestinationOrBuilder getStatsAnomaliesBaseDirectoryOrBuilder()Stats anomalies base folder path.
.google.cloud.aiplatform.v1.GcsDestination stats_anomalies_base_directory = 20;
| Type | Description |
| GcsDestinationOrBuilder |
getUpdateTime()
public Timestamp getUpdateTime()Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
.google.protobuf.Timestamp update_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| Timestamp | The updateTime. |
getUpdateTimeBuilder()
public Timestamp.Builder getUpdateTimeBuilder()Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
.google.protobuf.Timestamp update_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| Builder |
getUpdateTimeOrBuilder()
public TimestampOrBuilder getUpdateTimeOrBuilder()Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
.google.protobuf.Timestamp update_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| TimestampOrBuilder |
hasCreateTime()
public boolean hasCreateTime()Output only. Timestamp when this ModelDeploymentMonitoringJob was created.
.google.protobuf.Timestamp create_time = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| boolean | Whether the createTime field is set. |
hasEncryptionSpec()
public 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.v1.EncryptionSpec encryption_spec = 21;
| Type | Description |
| boolean | Whether the encryptionSpec field is set. |
hasError()
public 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];
| Type | Description |
| boolean | Whether the error field is set. |
hasLogTtl()
public 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;
| Type | Description |
| boolean | Whether the logTtl field is set. |
hasLoggingSamplingStrategy()
public boolean hasLoggingSamplingStrategy()Required. Sample Strategy for logging.
.google.cloud.aiplatform.v1.SamplingStrategy logging_sampling_strategy = 8 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| boolean | Whether the loggingSamplingStrategy field is set. |
hasModelDeploymentMonitoringScheduleConfig()
public boolean hasModelDeploymentMonitoringScheduleConfig()Required. Schedule config for running the monitoring job.
.google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig model_deployment_monitoring_schedule_config = 7 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| boolean | Whether the modelDeploymentMonitoringScheduleConfig field is set. |
hasModelMonitoringAlertConfig()
public boolean hasModelMonitoringAlertConfig()Alert config for model monitoring.
.google.cloud.aiplatform.v1.ModelMonitoringAlertConfig model_monitoring_alert_config = 15;
| Type | Description |
| boolean | Whether the modelMonitoringAlertConfig field is set. |
hasNextScheduleTime()
public 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];
| Type | Description |
| boolean | Whether the nextScheduleTime field is set. |
hasSamplePredictInstance()
public boolean hasSamplePredictInstance()Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests.
.google.protobuf.Value sample_predict_instance = 19;
| Type | Description |
| boolean | Whether the samplePredictInstance field is set. |
hasStatsAnomaliesBaseDirectory()
public boolean hasStatsAnomaliesBaseDirectory()Stats anomalies base folder path.
.google.cloud.aiplatform.v1.GcsDestination stats_anomalies_base_directory = 20;
| Type | Description |
| boolean | Whether the statsAnomaliesBaseDirectory field is set. |
hasUpdateTime()
public boolean hasUpdateTime()Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
.google.protobuf.Timestamp update_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Type | Description |
| boolean | Whether the updateTime field is set. |
internalGetFieldAccessorTable()
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()| Type | Description |
| FieldAccessorTable |
internalGetMapField(int number)
protected MapField internalGetMapField(int number)| Name | Description |
| number | int |
| Type | Description |
| MapField |
internalGetMutableMapField(int number)
protected MapField internalGetMutableMapField(int number)| Name | Description |
| number | int |
| Type | Description |
| MapField |
isInitialized()
public final boolean isInitialized()| Type | Description |
| boolean |
mergeCreateTime(Timestamp value)
public ModelDeploymentMonitoringJob.Builder mergeCreateTime(Timestamp value)Output only. Timestamp when this ModelDeploymentMonitoringJob was created.
.google.protobuf.Timestamp create_time = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| value | Timestamp |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
mergeEncryptionSpec(EncryptionSpec value)
public ModelDeploymentMonitoringJob.Builder mergeEncryptionSpec(EncryptionSpec value)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.v1.EncryptionSpec encryption_spec = 21;
| Name | Description |
| value | EncryptionSpec |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
mergeError(Status value)
public ModelDeploymentMonitoringJob.Builder mergeError(Status value) 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];
| Name | Description |
| value | com.google.rpc.Status |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
mergeFrom(ModelDeploymentMonitoringJob other)
public ModelDeploymentMonitoringJob.Builder mergeFrom(ModelDeploymentMonitoringJob other)| Name | Description |
| other | ModelDeploymentMonitoringJob |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
public ModelDeploymentMonitoringJob.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)| Name | Description |
| input | CodedInputStream |
| extensionRegistry | ExtensionRegistryLite |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
| Type | Description |
| IOException |
mergeFrom(Message other)
public ModelDeploymentMonitoringJob.Builder mergeFrom(Message other)| Name | Description |
| other | Message |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
mergeLogTtl(Duration value)
public ModelDeploymentMonitoringJob.Builder mergeLogTtl(Duration value)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;
| Name | Description |
| value | Duration |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
mergeLoggingSamplingStrategy(SamplingStrategy value)
public ModelDeploymentMonitoringJob.Builder mergeLoggingSamplingStrategy(SamplingStrategy value)Required. Sample Strategy for logging.
.google.cloud.aiplatform.v1.SamplingStrategy logging_sampling_strategy = 8 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| value | SamplingStrategy |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
mergeModelDeploymentMonitoringScheduleConfig(ModelDeploymentMonitoringScheduleConfig value)
public ModelDeploymentMonitoringJob.Builder mergeModelDeploymentMonitoringScheduleConfig(ModelDeploymentMonitoringScheduleConfig value)Required. Schedule config for running the monitoring job.
.google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig model_deployment_monitoring_schedule_config = 7 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| value | ModelDeploymentMonitoringScheduleConfig |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
mergeModelMonitoringAlertConfig(ModelMonitoringAlertConfig value)
public ModelDeploymentMonitoringJob.Builder mergeModelMonitoringAlertConfig(ModelMonitoringAlertConfig value)Alert config for model monitoring.
.google.cloud.aiplatform.v1.ModelMonitoringAlertConfig model_monitoring_alert_config = 15;
| Name | Description |
| value | ModelMonitoringAlertConfig |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
mergeNextScheduleTime(Timestamp value)
public ModelDeploymentMonitoringJob.Builder mergeNextScheduleTime(Timestamp value)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];
| Name | Description |
| value | Timestamp |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
mergeSamplePredictInstance(Value value)
public ModelDeploymentMonitoringJob.Builder mergeSamplePredictInstance(Value value)Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests.
.google.protobuf.Value sample_predict_instance = 19;
| Name | Description |
| value | Value |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
mergeStatsAnomaliesBaseDirectory(GcsDestination value)
public ModelDeploymentMonitoringJob.Builder mergeStatsAnomaliesBaseDirectory(GcsDestination value)Stats anomalies base folder path.
.google.cloud.aiplatform.v1.GcsDestination stats_anomalies_base_directory = 20;
| Name | Description |
| value | GcsDestination |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
mergeUnknownFields(UnknownFieldSet unknownFields)
public final ModelDeploymentMonitoringJob.Builder mergeUnknownFields(UnknownFieldSet unknownFields)| Name | Description |
| unknownFields | UnknownFieldSet |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
mergeUpdateTime(Timestamp value)
public ModelDeploymentMonitoringJob.Builder mergeUpdateTime(Timestamp value)Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
.google.protobuf.Timestamp update_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| value | Timestamp |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
putAllLabels(Map<String,String> values)
public ModelDeploymentMonitoringJob.Builder putAllLabels(Map<String,String> values)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;
| Name | Description |
| values | Map<String,String> |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
putLabels(String key, String value)
public ModelDeploymentMonitoringJob.Builder putLabels(String key, String value)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;
| Name | Description |
| key | String |
| value | String |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
removeBigqueryTables(int index)
public ModelDeploymentMonitoringJob.Builder removeBigqueryTables(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:
- Training data logging predict request/response
- Serving data logging predict request/response
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| index | int |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
removeLabels(String key)
public ModelDeploymentMonitoringJob.Builder removeLabels(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;
| Name | Description |
| key | String |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
removeModelDeploymentMonitoringObjectiveConfigs(int index)
public ModelDeploymentMonitoringJob.Builder removeModelDeploymentMonitoringObjectiveConfigs(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.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| index | int |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setAnalysisInstanceSchemaUri(String value)
public ModelDeploymentMonitoringJob.Builder setAnalysisInstanceSchemaUri(String value)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.
string analysis_instance_schema_uri = 16;
| Name | Description |
| value | StringThe analysisInstanceSchemaUri to set. |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
setAnalysisInstanceSchemaUriBytes(ByteString value)
public ModelDeploymentMonitoringJob.Builder setAnalysisInstanceSchemaUriBytes(ByteString value)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.
string analysis_instance_schema_uri = 16;
| Name | Description |
| value | ByteStringThe bytes for analysisInstanceSchemaUri to set. |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
setBigqueryTables(int index, ModelDeploymentMonitoringBigQueryTable value)
public ModelDeploymentMonitoringJob.Builder setBigqueryTables(int index, ModelDeploymentMonitoringBigQueryTable value)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:
- Training data logging predict request/response
- Serving data logging predict request/response
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| index | int |
| value | ModelDeploymentMonitoringBigQueryTable |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setBigqueryTables(int index, ModelDeploymentMonitoringBigQueryTable.Builder builderForValue)
public ModelDeploymentMonitoringJob.Builder setBigqueryTables(int index, ModelDeploymentMonitoringBigQueryTable.Builder builderForValue)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:
- Training data logging predict request/response
- Serving data logging predict request/response
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| index | int |
| builderForValue | ModelDeploymentMonitoringBigQueryTable.Builder |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setCreateTime(Timestamp value)
public ModelDeploymentMonitoringJob.Builder setCreateTime(Timestamp value)Output only. Timestamp when this ModelDeploymentMonitoringJob was created.
.google.protobuf.Timestamp create_time = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| value | Timestamp |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setCreateTime(Timestamp.Builder builderForValue)
public ModelDeploymentMonitoringJob.Builder setCreateTime(Timestamp.Builder builderForValue)Output only. Timestamp when this ModelDeploymentMonitoringJob was created.
.google.protobuf.Timestamp create_time = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| builderForValue | Builder |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setDisplayName(String value)
public ModelDeploymentMonitoringJob.Builder setDisplayName(String value)Required. The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.
string display_name = 2 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| value | StringThe displayName to set. |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
setDisplayNameBytes(ByteString value)
public ModelDeploymentMonitoringJob.Builder setDisplayNameBytes(ByteString value)Required. The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.
string display_name = 2 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| value | ByteStringThe bytes for displayName to set. |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
setEnableMonitoringPipelineLogs(boolean value)
public ModelDeploymentMonitoringJob.Builder setEnableMonitoringPipelineLogs(boolean value)If true, the scheduled monitoring pipeline status logs are sent to Google Cloud Logging. Please note the logs incur cost, which are subject to Cloud Logging pricing.
bool enable_monitoring_pipeline_logs = 22;
| Name | Description |
| value | booleanThe enableMonitoringPipelineLogs to set. |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
setEncryptionSpec(EncryptionSpec value)
public ModelDeploymentMonitoringJob.Builder setEncryptionSpec(EncryptionSpec value)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.v1.EncryptionSpec encryption_spec = 21;
| Name | Description |
| value | EncryptionSpec |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setEncryptionSpec(EncryptionSpec.Builder builderForValue)
public ModelDeploymentMonitoringJob.Builder setEncryptionSpec(EncryptionSpec.Builder builderForValue)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.v1.EncryptionSpec encryption_spec = 21;
| Name | Description |
| builderForValue | EncryptionSpec.Builder |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setEndpoint(String value)
public ModelDeploymentMonitoringJob.Builder setEndpoint(String value) Required. Endpoint resource name.
Format: projects/{project}/locations/{location}/endpoints/{endpoint}
string endpoint = 3 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }
| Name | Description |
| value | StringThe endpoint to set. |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
setEndpointBytes(ByteString value)
public ModelDeploymentMonitoringJob.Builder setEndpointBytes(ByteString value) Required. Endpoint resource name.
Format: projects/{project}/locations/{location}/endpoints/{endpoint}
string endpoint = 3 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }
| Name | Description |
| value | ByteStringThe bytes for endpoint to set. |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
setError(Status value)
public ModelDeploymentMonitoringJob.Builder setError(Status value) 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];
| Name | Description |
| value | com.google.rpc.Status |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setError(Status.Builder builderForValue)
public ModelDeploymentMonitoringJob.Builder setError(Status.Builder builderForValue) 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];
| Name | Description |
| builderForValue | com.google.rpc.Status.Builder |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setField(Descriptors.FieldDescriptor field, Object value)
public ModelDeploymentMonitoringJob.Builder setField(Descriptors.FieldDescriptor field, Object value)| Name | Description |
| field | FieldDescriptor |
| value | Object |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setLogTtl(Duration value)
public ModelDeploymentMonitoringJob.Builder setLogTtl(Duration value)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;
| Name | Description |
| value | Duration |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setLogTtl(Duration.Builder builderForValue)
public ModelDeploymentMonitoringJob.Builder setLogTtl(Duration.Builder builderForValue)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;
| Name | Description |
| builderForValue | Builder |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setLoggingSamplingStrategy(SamplingStrategy value)
public ModelDeploymentMonitoringJob.Builder setLoggingSamplingStrategy(SamplingStrategy value)Required. Sample Strategy for logging.
.google.cloud.aiplatform.v1.SamplingStrategy logging_sampling_strategy = 8 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| value | SamplingStrategy |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setLoggingSamplingStrategy(SamplingStrategy.Builder builderForValue)
public ModelDeploymentMonitoringJob.Builder setLoggingSamplingStrategy(SamplingStrategy.Builder builderForValue)Required. Sample Strategy for logging.
.google.cloud.aiplatform.v1.SamplingStrategy logging_sampling_strategy = 8 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| builderForValue | SamplingStrategy.Builder |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setModelDeploymentMonitoringObjectiveConfigs(int index, ModelDeploymentMonitoringObjectiveConfig value)
public ModelDeploymentMonitoringJob.Builder setModelDeploymentMonitoringObjectiveConfigs(int index, ModelDeploymentMonitoringObjectiveConfig value)Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| index | int |
| value | ModelDeploymentMonitoringObjectiveConfig |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setModelDeploymentMonitoringObjectiveConfigs(int index, ModelDeploymentMonitoringObjectiveConfig.Builder builderForValue)
public ModelDeploymentMonitoringJob.Builder setModelDeploymentMonitoringObjectiveConfigs(int index, ModelDeploymentMonitoringObjectiveConfig.Builder builderForValue)Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| index | int |
| builderForValue | ModelDeploymentMonitoringObjectiveConfig.Builder |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setModelDeploymentMonitoringScheduleConfig(ModelDeploymentMonitoringScheduleConfig value)
public ModelDeploymentMonitoringJob.Builder setModelDeploymentMonitoringScheduleConfig(ModelDeploymentMonitoringScheduleConfig value)Required. Schedule config for running the monitoring job.
.google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig model_deployment_monitoring_schedule_config = 7 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| value | ModelDeploymentMonitoringScheduleConfig |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setModelDeploymentMonitoringScheduleConfig(ModelDeploymentMonitoringScheduleConfig.Builder builderForValue)
public ModelDeploymentMonitoringJob.Builder setModelDeploymentMonitoringScheduleConfig(ModelDeploymentMonitoringScheduleConfig.Builder builderForValue)Required. Schedule config for running the monitoring job.
.google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig model_deployment_monitoring_schedule_config = 7 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
| builderForValue | ModelDeploymentMonitoringScheduleConfig.Builder |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setModelMonitoringAlertConfig(ModelMonitoringAlertConfig value)
public ModelDeploymentMonitoringJob.Builder setModelMonitoringAlertConfig(ModelMonitoringAlertConfig value)Alert config for model monitoring.
.google.cloud.aiplatform.v1.ModelMonitoringAlertConfig model_monitoring_alert_config = 15;
| Name | Description |
| value | ModelMonitoringAlertConfig |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setModelMonitoringAlertConfig(ModelMonitoringAlertConfig.Builder builderForValue)
public ModelDeploymentMonitoringJob.Builder setModelMonitoringAlertConfig(ModelMonitoringAlertConfig.Builder builderForValue)Alert config for model monitoring.
.google.cloud.aiplatform.v1.ModelMonitoringAlertConfig model_monitoring_alert_config = 15;
| Name | Description |
| builderForValue | ModelMonitoringAlertConfig.Builder |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setName(String value)
public ModelDeploymentMonitoringJob.Builder setName(String value)Output only. Resource name of a ModelDeploymentMonitoringJob.
string name = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| value | StringThe name to set. |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
setNameBytes(ByteString value)
public ModelDeploymentMonitoringJob.Builder setNameBytes(ByteString value)Output only. Resource name of a ModelDeploymentMonitoringJob.
string name = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| value | ByteStringThe bytes for name to set. |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
setNextScheduleTime(Timestamp value)
public ModelDeploymentMonitoringJob.Builder setNextScheduleTime(Timestamp value)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];
| Name | Description |
| value | Timestamp |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setNextScheduleTime(Timestamp.Builder builderForValue)
public ModelDeploymentMonitoringJob.Builder setNextScheduleTime(Timestamp.Builder builderForValue)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];
| Name | Description |
| builderForValue | Builder |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setPredictInstanceSchemaUri(String value)
public ModelDeploymentMonitoringJob.Builder setPredictInstanceSchemaUri(String value)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;
| Name | Description |
| value | StringThe predictInstanceSchemaUri to set. |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
setPredictInstanceSchemaUriBytes(ByteString value)
public ModelDeploymentMonitoringJob.Builder setPredictInstanceSchemaUriBytes(ByteString value)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;
| Name | Description |
| value | ByteStringThe bytes for predictInstanceSchemaUri to set. |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
public ModelDeploymentMonitoringJob.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)| Name | Description |
| field | FieldDescriptor |
| index | int |
| value | Object |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setSamplePredictInstance(Value value)
public ModelDeploymentMonitoringJob.Builder setSamplePredictInstance(Value value)Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests.
.google.protobuf.Value sample_predict_instance = 19;
| Name | Description |
| value | Value |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setSamplePredictInstance(Value.Builder builderForValue)
public ModelDeploymentMonitoringJob.Builder setSamplePredictInstance(Value.Builder builderForValue)Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests.
.google.protobuf.Value sample_predict_instance = 19;
| Name | Description |
| builderForValue | Builder |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setScheduleState(ModelDeploymentMonitoringJob.MonitoringScheduleState value)
public ModelDeploymentMonitoringJob.Builder setScheduleState(ModelDeploymentMonitoringJob.MonitoringScheduleState value)Output only. Schedule state when the monitoring job is in Running state.
.google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.MonitoringScheduleState schedule_state = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| value | ModelDeploymentMonitoringJob.MonitoringScheduleStateThe scheduleState to set. |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
setScheduleStateValue(int value)
public ModelDeploymentMonitoringJob.Builder setScheduleStateValue(int value)Output only. Schedule state when the monitoring job is in Running state.
.google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.MonitoringScheduleState schedule_state = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| value | intThe enum numeric value on the wire for scheduleState to set. |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
setState(JobState value)
public ModelDeploymentMonitoringJob.Builder setState(JobState value)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.v1.JobState state = 4 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| value | JobStateThe state to set. |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
setStateValue(int value)
public ModelDeploymentMonitoringJob.Builder setStateValue(int value)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.v1.JobState state = 4 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| value | intThe enum numeric value on the wire for state to set. |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder | This builder for chaining. |
setStatsAnomaliesBaseDirectory(GcsDestination value)
public ModelDeploymentMonitoringJob.Builder setStatsAnomaliesBaseDirectory(GcsDestination value)Stats anomalies base folder path.
.google.cloud.aiplatform.v1.GcsDestination stats_anomalies_base_directory = 20;
| Name | Description |
| value | GcsDestination |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setStatsAnomaliesBaseDirectory(GcsDestination.Builder builderForValue)
public ModelDeploymentMonitoringJob.Builder setStatsAnomaliesBaseDirectory(GcsDestination.Builder builderForValue)Stats anomalies base folder path.
.google.cloud.aiplatform.v1.GcsDestination stats_anomalies_base_directory = 20;
| Name | Description |
| builderForValue | GcsDestination.Builder |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setUnknownFields(UnknownFieldSet unknownFields)
public final ModelDeploymentMonitoringJob.Builder setUnknownFields(UnknownFieldSet unknownFields)| Name | Description |
| unknownFields | UnknownFieldSet |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setUpdateTime(Timestamp value)
public ModelDeploymentMonitoringJob.Builder setUpdateTime(Timestamp value)Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
.google.protobuf.Timestamp update_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| value | Timestamp |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |
setUpdateTime(Timestamp.Builder builderForValue)
public ModelDeploymentMonitoringJob.Builder setUpdateTime(Timestamp.Builder builderForValue)Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
.google.protobuf.Timestamp update_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];
| Name | Description |
| builderForValue | Builder |
| Type | Description |
| ModelDeploymentMonitoringJob.Builder |