Class ModelDeploymentMonitoringJob.Builder (2.4.0)

public static final class ModelDeploymentMonitoringJob.Builder extends GeneratedMessageV3.Builder<ModelDeploymentMonitoringJob.Builder> implements ModelDeploymentMonitoringJobOrBuilder

Represents 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

Methods

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:

  1. Training data logging predict request/response
  2. Serving data logging predict request/response

repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
NameDescription
valuesIterable<? extends com.google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable>
Returns
TypeDescription
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];

Parameter
NameDescription
valuesIterable<? extends com.google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig>
Returns
TypeDescription
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:

  1. Training data logging predict request/response
  2. Serving data logging predict request/response

repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
NameDescription
valueModelDeploymentMonitoringBigQueryTable
Returns
TypeDescription
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:

  1. Training data logging predict request/response
  2. Serving data logging predict request/response

repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
NameDescription
builderForValueModelDeploymentMonitoringBigQueryTable.Builder
Returns
TypeDescription
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:

  1. Training data logging predict request/response
  2. Serving data logging predict request/response

repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameters
NameDescription
indexint
valueModelDeploymentMonitoringBigQueryTable
Returns
TypeDescription
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:

  1. Training data logging predict request/response
  2. Serving data logging predict request/response

repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameters
NameDescription
indexint
builderForValueModelDeploymentMonitoringBigQueryTable.Builder
Returns
TypeDescription
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:

  1. Training data logging predict request/response
  2. Serving data logging predict request/response

repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
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:

  1. Training data logging predict request/response
  2. Serving data logging predict request/response

repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
NameDescription
indexint
Returns
TypeDescription
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];

Parameter
NameDescription
valueModelDeploymentMonitoringObjectiveConfig
Returns
TypeDescription
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];

Parameter
NameDescription
builderForValueModelDeploymentMonitoringObjectiveConfig.Builder
Returns
TypeDescription
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];

Parameters
NameDescription
indexint
valueModelDeploymentMonitoringObjectiveConfig
Returns
TypeDescription
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];

Parameters
NameDescription
indexint
builderForValueModelDeploymentMonitoringObjectiveConfig.Builder
Returns
TypeDescription
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];

Returns
TypeDescription
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];

Parameter
NameDescription
indexint
Returns
TypeDescription
ModelDeploymentMonitoringObjectiveConfig.Builder

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public ModelDeploymentMonitoringJob.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)
Parameters
NameDescription
fieldFieldDescriptor
valueObject
Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder
Overrides

build()

public ModelDeploymentMonitoringJob build()
Returns
TypeDescription
ModelDeploymentMonitoringJob

buildPartial()

public ModelDeploymentMonitoringJob buildPartial()
Returns
TypeDescription
ModelDeploymentMonitoringJob

clear()

public ModelDeploymentMonitoringJob.Builder clear()
Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder
Overrides

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;

Returns
TypeDescription
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:

  1. Training data logging predict request/response
  2. Serving data logging predict request/response

repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
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;

Returns
TypeDescription
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;

Returns
TypeDescription
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) = { ... }

Returns
TypeDescription
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];

Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder

clearField(Descriptors.FieldDescriptor field)

public ModelDeploymentMonitoringJob.Builder clearField(Descriptors.FieldDescriptor field)
Parameter
NameDescription
fieldFieldDescriptor
Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder
Overrides

clearLabels()

public ModelDeploymentMonitoringJob.Builder clearLabels()
Returns
TypeDescription
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;

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder

clearModelMonitoringAlertConfig()

public ModelDeploymentMonitoringJob.Builder clearModelMonitoringAlertConfig()

Alert config for model monitoring.

.google.cloud.aiplatform.v1.ModelMonitoringAlertConfig model_monitoring_alert_config = 15;

Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder

clearName()

public ModelDeploymentMonitoringJob.Builder clearName()

Output only. Resource name of a ModelDeploymentMonitoringJob.

string name = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
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];

Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder

clearOneof(Descriptors.OneofDescriptor oneof)

public ModelDeploymentMonitoringJob.Builder clearOneof(Descriptors.OneofDescriptor oneof)
Parameter
NameDescription
oneofOneofDescriptor
Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder
Overrides

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;

Returns
TypeDescription
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;

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
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;

Returns
TypeDescription
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];

Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder

clone()

public ModelDeploymentMonitoringJob.Builder clone()
Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder
Overrides

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;

Parameter
NameDescription
keyString
Returns
TypeDescription
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;

Returns
TypeDescription
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;

Returns
TypeDescription
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:

  1. Training data logging predict request/response
  2. Serving data logging predict request/response

repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
NameDescription
indexint
Returns
TypeDescription
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:

  1. Training data logging predict request/response
  2. Serving data logging predict request/response

repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
NameDescription
indexint
Returns
TypeDescription
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:

  1. Training data logging predict request/response
  2. Serving data logging predict request/response

repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
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:

  1. Training data logging predict request/response
  2. Serving data logging predict request/response

repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
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:

  1. Training data logging predict request/response
  2. Serving data logging predict request/response

repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
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:

  1. Training data logging predict request/response
  2. Serving data logging predict request/response

repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
NameDescription
indexint
Returns
TypeDescription
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:

  1. Training data logging predict request/response
  2. Serving data logging predict request/response

repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
TimestampOrBuilder

getDefaultInstanceForType()

public ModelDeploymentMonitoringJob getDefaultInstanceForType()
Returns
TypeDescription
ModelDeploymentMonitoringJob

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()
Returns
TypeDescription
Descriptor

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()
Returns
TypeDescription
Descriptor
Overrides

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];

Returns
TypeDescription
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];

Returns
TypeDescription
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;

Returns
TypeDescription
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;

Returns
TypeDescription
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;

Returns
TypeDescription
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;

Returns
TypeDescription
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) = { ... }

Returns
TypeDescription
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) = { ... }

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
com.google.rpc.StatusOrBuilder

getLabels()

public Map<String,String> getLabels()

Use #getLabelsMap() instead.

Returns
TypeDescription
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;

Returns
TypeDescription
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;

Returns
TypeDescription
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;

Parameters
NameDescription
keyString
defaultValueString
Returns
TypeDescription
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;

Parameter
NameDescription
keyString
Returns
TypeDescription
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;

Returns
TypeDescription
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;

Returns
TypeDescription
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;

Returns
TypeDescription
DurationOrBuilder

getLoggingSamplingStrategy()

public SamplingStrategy getLoggingSamplingStrategy()

Required. Sample Strategy for logging.

.google.cloud.aiplatform.v1.SamplingStrategy logging_sampling_strategy = 8 [(.google.api.field_behavior) = REQUIRED];

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Parameter
NameDescription
indexint
Returns
TypeDescription
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];

Parameter
NameDescription
indexint
Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Parameter
NameDescription
indexint
Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
ModelDeploymentMonitoringScheduleConfigOrBuilder

getModelMonitoringAlertConfig()

public ModelMonitoringAlertConfig getModelMonitoringAlertConfig()

Alert config for model monitoring.

.google.cloud.aiplatform.v1.ModelMonitoringAlertConfig model_monitoring_alert_config = 15;

Returns
TypeDescription
ModelMonitoringAlertConfig

The modelMonitoringAlertConfig.

getModelMonitoringAlertConfigBuilder()

public ModelMonitoringAlertConfig.Builder getModelMonitoringAlertConfigBuilder()

Alert config for model monitoring.

.google.cloud.aiplatform.v1.ModelMonitoringAlertConfig model_monitoring_alert_config = 15;

Returns
TypeDescription
ModelMonitoringAlertConfig.Builder

getModelMonitoringAlertConfigOrBuilder()

public ModelMonitoringAlertConfigOrBuilder getModelMonitoringAlertConfigOrBuilder()

Alert config for model monitoring.

.google.cloud.aiplatform.v1.ModelMonitoringAlertConfig model_monitoring_alert_config = 15;

Returns
TypeDescription
ModelMonitoringAlertConfigOrBuilder

getMutableLabels()

public Map<String,String> getMutableLabels()

Use alternate mutation accessors instead.

Returns
TypeDescription
Map<String,String>

getName()

public String getName()

Output only. Resource name of a ModelDeploymentMonitoringJob.

string name = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
String

The name.

getNameBytes()

public ByteString getNameBytes()

Output only. Resource name of a ModelDeploymentMonitoringJob.

string name = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
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;

Returns
TypeDescription
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;

Returns
TypeDescription
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;

Returns
TypeDescription
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;

Returns
TypeDescription
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;

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
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;

Returns
TypeDescription
GcsDestination

The statsAnomaliesBaseDirectory.

getStatsAnomaliesBaseDirectoryBuilder()

public GcsDestination.Builder getStatsAnomaliesBaseDirectoryBuilder()

Stats anomalies base folder path.

.google.cloud.aiplatform.v1.GcsDestination stats_anomalies_base_directory = 20;

Returns
TypeDescription
GcsDestination.Builder

getStatsAnomaliesBaseDirectoryOrBuilder()

public GcsDestinationOrBuilder getStatsAnomaliesBaseDirectoryOrBuilder()

Stats anomalies base folder path.

.google.cloud.aiplatform.v1.GcsDestination stats_anomalies_base_directory = 20;

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
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;

Returns
TypeDescription
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];

Returns
TypeDescription
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;

Returns
TypeDescription
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];

Returns
TypeDescription
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];

Returns
TypeDescription
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;

Returns
TypeDescription
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];

Returns
TypeDescription
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;

Returns
TypeDescription
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;

Returns
TypeDescription
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];

Returns
TypeDescription
boolean

Whether the updateTime field is set.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
TypeDescription
FieldAccessorTable
Overrides

internalGetMapField(int number)

protected MapField internalGetMapField(int number)
Parameter
NameDescription
numberint
Returns
TypeDescription
MapField
Overrides

internalGetMutableMapField(int number)

protected MapField internalGetMutableMapField(int number)
Parameter
NameDescription
numberint
Returns
TypeDescription
MapField
Overrides

isInitialized()

public final boolean isInitialized()
Returns
TypeDescription
boolean
Overrides

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];

Parameter
NameDescription
valueTimestamp
Returns
TypeDescription
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;

Parameter
NameDescription
valueEncryptionSpec
Returns
TypeDescription
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];

Parameter
NameDescription
valuecom.google.rpc.Status
Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder

mergeFrom(ModelDeploymentMonitoringJob other)

public ModelDeploymentMonitoringJob.Builder mergeFrom(ModelDeploymentMonitoringJob other)
Parameter
NameDescription
otherModelDeploymentMonitoringJob
Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public ModelDeploymentMonitoringJob.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputCodedInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder
Overrides Exceptions
TypeDescription
IOException

mergeFrom(Message other)

public ModelDeploymentMonitoringJob.Builder mergeFrom(Message other)
Parameter
NameDescription
otherMessage
Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder
Overrides

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;

Parameter
NameDescription
valueDuration
Returns
TypeDescription
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];

Parameter
NameDescription
valueSamplingStrategy
Returns
TypeDescription
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];

Parameter
NameDescription
valueModelDeploymentMonitoringScheduleConfig
Returns
TypeDescription
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;

Parameter
NameDescription
valueModelMonitoringAlertConfig
Returns
TypeDescription
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];

Parameter
NameDescription
valueTimestamp
Returns
TypeDescription
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;

Parameter
NameDescription
valueValue
Returns
TypeDescription
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;

Parameter
NameDescription
valueGcsDestination
Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder

mergeUnknownFields(UnknownFieldSet unknownFields)

public final ModelDeploymentMonitoringJob.Builder mergeUnknownFields(UnknownFieldSet unknownFields)
Parameter
NameDescription
unknownFieldsUnknownFieldSet
Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder
Overrides

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];

Parameter
NameDescription
valueTimestamp
Returns
TypeDescription
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;

Parameter
NameDescription
valuesMap<String,String>
Returns
TypeDescription
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;

Parameters
NameDescription
keyString
valueString
Returns
TypeDescription
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:

  1. Training data logging predict request/response
  2. Serving data logging predict request/response

repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
NameDescription
indexint
Returns
TypeDescription
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;

Parameter
NameDescription
keyString
Returns
TypeDescription
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];

Parameter
NameDescription
indexint
Returns
TypeDescription
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;

Parameter
NameDescription
valueString

The analysisInstanceSchemaUri to set.

Returns
TypeDescription
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;

Parameter
NameDescription
valueByteString

The bytes for analysisInstanceSchemaUri to set.

Returns
TypeDescription
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:

  1. Training data logging predict request/response
  2. Serving data logging predict request/response

repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameters
NameDescription
indexint
valueModelDeploymentMonitoringBigQueryTable
Returns
TypeDescription
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:

  1. Training data logging predict request/response
  2. Serving data logging predict request/response

repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameters
NameDescription
indexint
builderForValueModelDeploymentMonitoringBigQueryTable.Builder
Returns
TypeDescription
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];

Parameter
NameDescription
valueTimestamp
Returns
TypeDescription
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];

Parameter
NameDescription
builderForValueBuilder
Returns
TypeDescription
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];

Parameter
NameDescription
valueString

The displayName to set.

Returns
TypeDescription
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];

Parameter
NameDescription
valueByteString

The bytes for displayName to set.

Returns
TypeDescription
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;

Parameter
NameDescription
valueboolean

The enableMonitoringPipelineLogs to set.

Returns
TypeDescription
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;

Parameter
NameDescription
valueEncryptionSpec
Returns
TypeDescription
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;

Parameter
NameDescription
builderForValueEncryptionSpec.Builder
Returns
TypeDescription
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) = { ... }

Parameter
NameDescription
valueString

The endpoint to set.

Returns
TypeDescription
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) = { ... }

Parameter
NameDescription
valueByteString

The bytes for endpoint to set.

Returns
TypeDescription
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];

Parameter
NameDescription
valuecom.google.rpc.Status
Returns
TypeDescription
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];

Parameter
NameDescription
builderForValuecom.google.rpc.Status.Builder
Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder

setField(Descriptors.FieldDescriptor field, Object value)

public ModelDeploymentMonitoringJob.Builder setField(Descriptors.FieldDescriptor field, Object value)
Parameters
NameDescription
fieldFieldDescriptor
valueObject
Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder
Overrides

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;

Parameter
NameDescription
valueDuration
Returns
TypeDescription
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;

Parameter
NameDescription
builderForValueBuilder
Returns
TypeDescription
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];

Parameter
NameDescription
valueSamplingStrategy
Returns
TypeDescription
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];

Parameter
NameDescription
builderForValueSamplingStrategy.Builder
Returns
TypeDescription
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];

Parameters
NameDescription
indexint
valueModelDeploymentMonitoringObjectiveConfig
Returns
TypeDescription
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];

Parameters
NameDescription
indexint
builderForValueModelDeploymentMonitoringObjectiveConfig.Builder
Returns
TypeDescription
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];

Parameter
NameDescription
valueModelDeploymentMonitoringScheduleConfig
Returns
TypeDescription
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];

Parameter
NameDescription
builderForValueModelDeploymentMonitoringScheduleConfig.Builder
Returns
TypeDescription
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;

Parameter
NameDescription
valueModelMonitoringAlertConfig
Returns
TypeDescription
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;

Parameter
NameDescription
builderForValueModelMonitoringAlertConfig.Builder
Returns
TypeDescription
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];

Parameter
NameDescription
valueString

The name to set.

Returns
TypeDescription
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];

Parameter
NameDescription
valueByteString

The bytes for name to set.

Returns
TypeDescription
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];

Parameter
NameDescription
valueTimestamp
Returns
TypeDescription
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];

Parameter
NameDescription
builderForValueBuilder
Returns
TypeDescription
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;

Parameter
NameDescription
valueString

The predictInstanceSchemaUri to set.

Returns
TypeDescription
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;

Parameter
NameDescription
valueByteString

The bytes for predictInstanceSchemaUri to set.

Returns
TypeDescription
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)
Parameters
NameDescription
fieldFieldDescriptor
indexint
valueObject
Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder
Overrides

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;

Parameter
NameDescription
valueValue
Returns
TypeDescription
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;

Parameter
NameDescription
builderForValueBuilder
Returns
TypeDescription
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];

Parameter
NameDescription
valueModelDeploymentMonitoringJob.MonitoringScheduleState

The scheduleState to set.

Returns
TypeDescription
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];

Parameter
NameDescription
valueint

The enum numeric value on the wire for scheduleState to set.

Returns
TypeDescription
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];

Parameter
NameDescription
valueJobState

The state to set.

Returns
TypeDescription
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];

Parameter
NameDescription
valueint

The enum numeric value on the wire for state to set.

Returns
TypeDescription
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;

Parameter
NameDescription
valueGcsDestination
Returns
TypeDescription
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;

Parameter
NameDescription
builderForValueGcsDestination.Builder
Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder

setUnknownFields(UnknownFieldSet unknownFields)

public final ModelDeploymentMonitoringJob.Builder setUnknownFields(UnknownFieldSet unknownFields)
Parameter
NameDescription
unknownFieldsUnknownFieldSet
Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder
Overrides

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];

Parameter
NameDescription
valueTimestamp
Returns
TypeDescription
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];

Parameter
NameDescription
builderForValueBuilder
Returns
TypeDescription
ModelDeploymentMonitoringJob.Builder