Interface ModelMonitorOrBuilder (3.43.0) 

public interface ModelMonitorOrBuilder extends MessageOrBuilder

Implements

MessageOrBuilder

Methods

getCreateTime()

public abstract Timestamp getCreateTime()

Output only. Timestamp when this ModelMonitor was created.

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

Returns
Type Description
Timestamp

The createTime.

getCreateTimeOrBuilder()

public abstract TimestampOrBuilder getCreateTimeOrBuilder()

Output only. Timestamp when this ModelMonitor was created.

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

Returns
Type Description
TimestampOrBuilder

getDefaultObjectiveCase()

public abstract ModelMonitor.DefaultObjectiveCase getDefaultObjectiveCase()
Returns
Type Description
ModelMonitor.DefaultObjectiveCase

getDisplayName()

public abstract 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
Type Description
String

The displayName.

getDisplayNameBytes()

public abstract 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
Type Description
ByteString

The bytes for displayName.

getExplanationSpec()

public abstract ExplanationSpec getExplanationSpec()

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

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

Returns
Type Description
ExplanationSpec

The explanationSpec.

getExplanationSpecOrBuilder()

public abstract ExplanationSpecOrBuilder getExplanationSpecOrBuilder()

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

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

Returns
Type Description
ExplanationSpecOrBuilder

getModelMonitoringSchema()

public abstract 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
Type Description
ModelMonitoringSchema

The modelMonitoringSchema.

getModelMonitoringSchemaOrBuilder()

public abstract 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;

Returns
Type Description
ModelMonitoringSchemaOrBuilder

getModelMonitoringTarget()

public abstract 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
Type Description
ModelMonitor.ModelMonitoringTarget

The modelMonitoringTarget.

getModelMonitoringTargetOrBuilder()

public abstract 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;

Returns
Type Description
ModelMonitor.ModelMonitoringTargetOrBuilder

getName()

public abstract 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
Type Description
String

The name.

getNameBytes()

public abstract 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
Type Description
ByteString

The bytes for name.

getNotificationSpec()

public abstract ModelMonitoringNotificationSpec getNotificationSpec()

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

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

Returns
Type Description
ModelMonitoringNotificationSpec

The notificationSpec.

getNotificationSpecOrBuilder()

public abstract ModelMonitoringNotificationSpecOrBuilder getNotificationSpecOrBuilder()

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

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

Returns
Type Description
ModelMonitoringNotificationSpecOrBuilder

getOutputSpec()

public abstract 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
Type Description
ModelMonitoringOutputSpec

The outputSpec.

getOutputSpecOrBuilder()

public abstract 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;

Returns
Type Description
ModelMonitoringOutputSpecOrBuilder

getTabularObjective()

public abstract ModelMonitoringObjectiveSpec.TabularObjective getTabularObjective()

Optional default tabular model monitoring objective.

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

Returns
Type Description
ModelMonitoringObjectiveSpec.TabularObjective

The tabularObjective.

getTabularObjectiveOrBuilder()

public abstract ModelMonitoringObjectiveSpec.TabularObjectiveOrBuilder getTabularObjectiveOrBuilder()

Optional default tabular model monitoring objective.

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

Returns
Type Description
ModelMonitoringObjectiveSpec.TabularObjectiveOrBuilder

getTrainingDataset()

public abstract 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
Type Description
ModelMonitoringInput

The trainingDataset.

getTrainingDatasetOrBuilder()

public abstract 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;

Returns
Type Description
ModelMonitoringInputOrBuilder

getUpdateTime()

public abstract 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
Type Description
Timestamp

The updateTime.

getUpdateTimeOrBuilder()

public abstract TimestampOrBuilder getUpdateTimeOrBuilder()

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

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

Returns
Type Description
TimestampOrBuilder

hasCreateTime()

public abstract boolean hasCreateTime()

Output only. Timestamp when this ModelMonitor was created.

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

Returns
Type Description
boolean

Whether the createTime field is set.

hasExplanationSpec()

public abstract boolean hasExplanationSpec()

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

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

Returns
Type Description
boolean

Whether the explanationSpec field is set.

hasModelMonitoringSchema()

public abstract 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
Type Description
boolean

Whether the modelMonitoringSchema field is set.

hasModelMonitoringTarget()

public abstract 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
Type Description
boolean

Whether the modelMonitoringTarget field is set.

hasNotificationSpec()

public abstract boolean hasNotificationSpec()

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

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

Returns
Type Description
boolean

Whether the notificationSpec field is set.

hasOutputSpec()

public abstract 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
Type Description
boolean

Whether the outputSpec field is set.

hasTabularObjective()

public abstract boolean hasTabularObjective()

Optional default tabular model monitoring objective.

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

Returns
Type Description
boolean

Whether the tabularObjective field is set.

hasTrainingDataset()

public abstract 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
Type Description
boolean

Whether the trainingDataset field is set.

hasUpdateTime()

public abstract 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
Type Description
boolean

Whether the updateTime field is set.