com.google.cloud.aiplatform.v1beta1

Interface ModelMonitorOrBuilder

All Superinterfaces:

com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder

All Known Implementing Classes:

ModelMonitor, ModelMonitor.Builder

public interface ModelMonitorOrBuilder
extends com.google.protobuf.MessageOrBuilder

Method Summary

Modifier and Type	Method and Description
com.google.protobuf.Timestamp	getCreateTime() Output only.
com.google.protobuf.TimestampOrBuilder	getCreateTimeOrBuilder() Output only.
ModelMonitor.DefaultObjectiveCase	getDefaultObjectiveCase()
String	getDisplayName() The display name of the ModelMonitor.
com.google.protobuf.ByteString	getDisplayNameBytes() The display name of the ModelMonitor.
ExplanationSpec	getExplanationSpec() Optional model explanation spec.
ExplanationSpecOrBuilder	getExplanationSpecOrBuilder() Optional model explanation spec.
ModelMonitoringSchema	getModelMonitoringSchema() Monitoring Schema is to specify the model's features, prediction outputs and ground truth properties.
ModelMonitoringSchemaOrBuilder	getModelMonitoringSchemaOrBuilder() Monitoring Schema is to specify the model's features, prediction outputs and ground truth properties.
ModelMonitor.ModelMonitoringTarget	getModelMonitoringTarget() The entity that is subject to analysis.
ModelMonitor.ModelMonitoringTargetOrBuilder	getModelMonitoringTargetOrBuilder() The entity that is subject to analysis.
String	getName() Immutable.
com.google.protobuf.ByteString	getNameBytes() Immutable.
ModelMonitoringNotificationSpec	getNotificationSpec() Optional default notification spec, it can be overridden in the ModelMonitoringJob notification spec.
ModelMonitoringNotificationSpecOrBuilder	getNotificationSpecOrBuilder() Optional default notification spec, it can be overridden in the ModelMonitoringJob notification spec.
ModelMonitoringOutputSpec	getOutputSpec() Optional default monitoring metrics/logs export spec, it can be overridden in the ModelMonitoringJob output spec.
ModelMonitoringOutputSpecOrBuilder	getOutputSpecOrBuilder() Optional default monitoring metrics/logs export spec, it can be overridden in the ModelMonitoringJob output spec.
ModelMonitoringObjectiveSpec.TabularObjective	getTabularObjective() Optional default tabular model monitoring objective.
ModelMonitoringObjectiveSpec.TabularObjectiveOrBuilder	getTabularObjectiveOrBuilder() Optional default tabular model monitoring objective.
ModelMonitoringInput	getTrainingDataset() Optional training dataset used to train the model.
ModelMonitoringInputOrBuilder	getTrainingDatasetOrBuilder() Optional training dataset used to train the model.
com.google.protobuf.Timestamp	getUpdateTime() Output only.
com.google.protobuf.TimestampOrBuilder	getUpdateTimeOrBuilder() Output only.
boolean	hasCreateTime() Output only.
boolean	hasExplanationSpec() Optional model explanation spec.
boolean	hasModelMonitoringSchema() Monitoring Schema is to specify the model's features, prediction outputs and ground truth properties.
boolean	hasModelMonitoringTarget() The entity that is subject to analysis.
boolean	hasNotificationSpec() Optional default notification spec, it can be overridden in the ModelMonitoringJob notification spec.
boolean	hasOutputSpec() Optional default monitoring metrics/logs export spec, it can be overridden in the ModelMonitoringJob output spec.
boolean	hasTabularObjective() Optional default tabular model monitoring objective.
boolean	hasTrainingDataset() Optional training dataset used to train the model.
boolean	hasUpdateTime() Output only.

Methods inherited from interface com.google.protobuf.MessageOrBuilder

findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder

isInitialized

Method Detail

hasTabularObjective

boolean hasTabularObjective()

 Optional default tabular model monitoring objective.
 

.google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveSpec.TabularObjective tabular_objective = 11;

Returns:

Whether the tabularObjective field is set.

getTabularObjective

ModelMonitoringObjectiveSpec.TabularObjective getTabularObjective()

 Optional default tabular model monitoring objective.
 

.google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveSpec.TabularObjective tabular_objective = 11;

Returns:

The tabularObjective.

getTabularObjectiveOrBuilder

ModelMonitoringObjectiveSpec.TabularObjectiveOrBuilder getTabularObjectiveOrBuilder()

 Optional default tabular model monitoring objective.
 

.google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveSpec.TabularObjective tabular_objective = 11;

getName

String getName()

 Immutable. Resource name of the ModelMonitor. Format:
 `projects/{project}/locations/{location}/modelMonitors/{model_monitor}`.
 

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

Returns:

The name.

getNameBytes

com.google.protobuf.ByteString getNameBytes()

 Immutable. Resource name of the ModelMonitor. Format:
 `projects/{project}/locations/{location}/modelMonitors/{model_monitor}`.
 

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

Returns:

The bytes for name.

getDisplayName

String getDisplayName()

 The display name of the ModelMonitor.
 The name can be up to 128 characters long and can consist of any UTF-8.
 

string display_name = 2;

Returns:

The displayName.

getDisplayNameBytes

com.google.protobuf.ByteString getDisplayNameBytes()

 The display name of the ModelMonitor.
 The name can be up to 128 characters long and can consist of any UTF-8.
 

string display_name = 2;

Returns:

The bytes for displayName.

hasModelMonitoringTarget

boolean hasModelMonitoringTarget()

 The entity that is subject to analysis.
 Currently only models in Vertex AI Model Registry are supported. If you
 want to analyze the model which is outside the Vertex AI, you could
 register a model in Vertex AI Model Registry using just a display name.
 

.google.cloud.aiplatform.v1beta1.ModelMonitor.ModelMonitoringTarget model_monitoring_target = 3;

Returns:

Whether the modelMonitoringTarget field is set.

getModelMonitoringTarget

ModelMonitor.ModelMonitoringTarget getModelMonitoringTarget()

 The entity that is subject to analysis.
 Currently only models in Vertex AI Model Registry are supported. If you
 want to analyze the model which is outside the Vertex AI, you could
 register a model in Vertex AI Model Registry using just a display name.
 

.google.cloud.aiplatform.v1beta1.ModelMonitor.ModelMonitoringTarget model_monitoring_target = 3;

Returns:

The modelMonitoringTarget.

getModelMonitoringTargetOrBuilder

ModelMonitor.ModelMonitoringTargetOrBuilder getModelMonitoringTargetOrBuilder()

 The entity that is subject to analysis.
 Currently only models in Vertex AI Model Registry are supported. If you
 want to analyze the model which is outside the Vertex AI, you could
 register a model in Vertex AI Model Registry using just a display name.
 

.google.cloud.aiplatform.v1beta1.ModelMonitor.ModelMonitoringTarget model_monitoring_target = 3;

hasTrainingDataset

boolean hasTrainingDataset()

 Optional training dataset used to train the model.
 It can serve as a reference dataset to identify changes in production.
 

.google.cloud.aiplatform.v1beta1.ModelMonitoringInput training_dataset = 10;

Returns:

Whether the trainingDataset field is set.

getTrainingDataset

ModelMonitoringInput getTrainingDataset()

 Optional training dataset used to train the model.
 It can serve as a reference dataset to identify changes in production.
 

.google.cloud.aiplatform.v1beta1.ModelMonitoringInput training_dataset = 10;

Returns:

The trainingDataset.

getTrainingDatasetOrBuilder

ModelMonitoringInputOrBuilder getTrainingDatasetOrBuilder()

 Optional training dataset used to train the model.
 It can serve as a reference dataset to identify changes in production.
 

.google.cloud.aiplatform.v1beta1.ModelMonitoringInput training_dataset = 10;

hasNotificationSpec

boolean hasNotificationSpec()

 Optional default notification spec, it can be overridden in the
 ModelMonitoringJob notification spec.
 

.google.cloud.aiplatform.v1beta1.ModelMonitoringNotificationSpec notification_spec = 12;

Returns:

Whether the notificationSpec field is set.

getNotificationSpec

ModelMonitoringNotificationSpec getNotificationSpec()

 Optional default notification spec, it can be overridden in the
 ModelMonitoringJob notification spec.
 

.google.cloud.aiplatform.v1beta1.ModelMonitoringNotificationSpec notification_spec = 12;

Returns:

The notificationSpec.

getNotificationSpecOrBuilder

ModelMonitoringNotificationSpecOrBuilder getNotificationSpecOrBuilder()

 Optional default notification spec, it can be overridden in the
 ModelMonitoringJob notification spec.
 

.google.cloud.aiplatform.v1beta1.ModelMonitoringNotificationSpec notification_spec = 12;

hasOutputSpec

boolean hasOutputSpec()

 Optional default monitoring metrics/logs export spec, it can be overridden
 in the ModelMonitoringJob output spec.
 If not specified, a default Google Cloud Storage bucket will be created
 under your project.
 

.google.cloud.aiplatform.v1beta1.ModelMonitoringOutputSpec output_spec = 13;

Returns:

Whether the outputSpec field is set.

getOutputSpec

ModelMonitoringOutputSpec getOutputSpec()

 Optional default monitoring metrics/logs export spec, it can be overridden
 in the ModelMonitoringJob output spec.
 If not specified, a default Google Cloud Storage bucket will be created
 under your project.
 

.google.cloud.aiplatform.v1beta1.ModelMonitoringOutputSpec output_spec = 13;

Returns:

The outputSpec.

getOutputSpecOrBuilder

ModelMonitoringOutputSpecOrBuilder getOutputSpecOrBuilder()

 Optional default monitoring metrics/logs export spec, it can be overridden
 in the ModelMonitoringJob output spec.
 If not specified, a default Google Cloud Storage bucket will be created
 under your project.
 

.google.cloud.aiplatform.v1beta1.ModelMonitoringOutputSpec output_spec = 13;

hasExplanationSpec

boolean hasExplanationSpec()

 Optional model explanation spec. It is used for feature attribution
 monitoring.
 

.google.cloud.aiplatform.v1beta1.ExplanationSpec explanation_spec = 16;

Returns:

Whether the explanationSpec field is set.

getExplanationSpec

ExplanationSpec getExplanationSpec()

 Optional model explanation spec. It is used for feature attribution
 monitoring.
 

.google.cloud.aiplatform.v1beta1.ExplanationSpec explanation_spec = 16;

Returns:

The explanationSpec.

getExplanationSpecOrBuilder

ExplanationSpecOrBuilder getExplanationSpecOrBuilder()

 Optional model explanation spec. It is used for feature attribution
 monitoring.
 

.google.cloud.aiplatform.v1beta1.ExplanationSpec explanation_spec = 16;

hasModelMonitoringSchema

boolean hasModelMonitoringSchema()

 Monitoring Schema is to specify the model's features, prediction outputs
 and ground truth properties. It is used to extract pertinent data from the
 dataset and to process features based on their properties.
 Make sure that the schema aligns with your dataset, if it does not, we will
 be unable to extract data from the dataset.
 It is required for most models, but optional for Vertex AI AutoML Tables
 unless the schem information is not available.
 

.google.cloud.aiplatform.v1beta1.ModelMonitoringSchema model_monitoring_schema = 9;

Returns:

Whether the modelMonitoringSchema field is set.

getModelMonitoringSchema

ModelMonitoringSchema getModelMonitoringSchema()

 Monitoring Schema is to specify the model's features, prediction outputs
 and ground truth properties. It is used to extract pertinent data from the
 dataset and to process features based on their properties.
 Make sure that the schema aligns with your dataset, if it does not, we will
 be unable to extract data from the dataset.
 It is required for most models, but optional for Vertex AI AutoML Tables
 unless the schem information is not available.
 

.google.cloud.aiplatform.v1beta1.ModelMonitoringSchema model_monitoring_schema = 9;

Returns:

The modelMonitoringSchema.

getModelMonitoringSchemaOrBuilder

ModelMonitoringSchemaOrBuilder getModelMonitoringSchemaOrBuilder()

 Monitoring Schema is to specify the model's features, prediction outputs
 and ground truth properties. It is used to extract pertinent data from the
 dataset and to process features based on their properties.
 Make sure that the schema aligns with your dataset, if it does not, we will
 be unable to extract data from the dataset.
 It is required for most models, but optional for Vertex AI AutoML Tables
 unless the schem information is not available.
 

.google.cloud.aiplatform.v1beta1.ModelMonitoringSchema model_monitoring_schema = 9;

hasCreateTime

boolean hasCreateTime()

 Output only. Timestamp when this ModelMonitor was created.
 

.google.protobuf.Timestamp create_time = 6 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns:

Whether the createTime field is set.

getCreateTime

com.google.protobuf.Timestamp getCreateTime()

 Output only. Timestamp when this ModelMonitor was created.
 

.google.protobuf.Timestamp create_time = 6 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns:

The createTime.

getCreateTimeOrBuilder

com.google.protobuf.TimestampOrBuilder getCreateTimeOrBuilder()

 Output only. Timestamp when this ModelMonitor was created.
 

.google.protobuf.Timestamp create_time = 6 [(.google.api.field_behavior) = OUTPUT_ONLY];

hasUpdateTime

boolean hasUpdateTime()

 Output only. Timestamp when this ModelMonitor was updated most recently.
 

.google.protobuf.Timestamp update_time = 7 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns:

Whether the updateTime field is set.

getUpdateTime

com.google.protobuf.Timestamp getUpdateTime()

 Output only. Timestamp when this ModelMonitor was updated most recently.
 

.google.protobuf.Timestamp update_time = 7 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns:

The updateTime.

getUpdateTimeOrBuilder

com.google.protobuf.TimestampOrBuilder getUpdateTimeOrBuilder()

 Output only. Timestamp when this ModelMonitor was updated most recently.
 

.google.protobuf.Timestamp update_time = 7 [(.google.api.field_behavior) = OUTPUT_ONLY];

getDefaultObjectiveCase

ModelMonitor.DefaultObjectiveCase getDefaultObjectiveCase()