Class ModelMonitor.Builder (3.52.0) 

public static final class ModelMonitor.Builder extends GeneratedMessageV3.Builder<ModelMonitor.Builder> implements ModelMonitorOrBuilder

Vertex AI Model Monitoring Service serves as a central hub for the analysis and visualization of data quality and performance related to models. ModelMonitor stands as a top level resource for overseeing your model monitoring tasks.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitor

Inheritance

Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > ModelMonitor.Builder

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()
Returns
Type Description
Descriptor

Methods

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public ModelMonitor.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)
Parameters
Name Description
field FieldDescriptor
value Object
Returns
Type Description
ModelMonitor.Builder
Overrides

build()

public ModelMonitor build()
Returns
Type Description
ModelMonitor

buildPartial()

public ModelMonitor buildPartial()
Returns
Type Description
ModelMonitor

clear()

public ModelMonitor.Builder clear()
Returns
Type Description
ModelMonitor.Builder
Overrides

clearCreateTime()

public ModelMonitor.Builder clearCreateTime()

Output only. Timestamp when this ModelMonitor was created.

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

Returns
Type Description
ModelMonitor.Builder

clearDefaultObjective()

public ModelMonitor.Builder clearDefaultObjective()
Returns
Type Description
ModelMonitor.Builder

clearDisplayName()

public ModelMonitor.Builder clearDisplayName()

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
ModelMonitor.Builder

This builder for chaining.

clearExplanationSpec()

public ModelMonitor.Builder clearExplanationSpec()

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

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

Returns
Type Description
ModelMonitor.Builder

clearField(Descriptors.FieldDescriptor field)

public ModelMonitor.Builder clearField(Descriptors.FieldDescriptor field)
Parameter
Name Description
field FieldDescriptor
Returns
Type Description
ModelMonitor.Builder
Overrides

clearModelMonitoringSchema()

public ModelMonitor.Builder clearModelMonitoringSchema()

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
ModelMonitor.Builder

clearModelMonitoringTarget()

public ModelMonitor.Builder clearModelMonitoringTarget()

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.Builder

clearName()

public ModelMonitor.Builder clearName()

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
ModelMonitor.Builder

This builder for chaining.

clearNotificationSpec()

public ModelMonitor.Builder clearNotificationSpec()

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

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

Returns
Type Description
ModelMonitor.Builder

clearOneof(Descriptors.OneofDescriptor oneof)

public ModelMonitor.Builder clearOneof(Descriptors.OneofDescriptor oneof)
Parameter
Name Description
oneof OneofDescriptor
Returns
Type Description
ModelMonitor.Builder
Overrides

clearOutputSpec()

public ModelMonitor.Builder clearOutputSpec()

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
ModelMonitor.Builder

clearSatisfiesPzi()

public ModelMonitor.Builder clearSatisfiesPzi()

Output only. Reserved for future use.

bool satisfies_pzi = 18 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type Description
ModelMonitor.Builder

This builder for chaining.

clearSatisfiesPzs()

public ModelMonitor.Builder clearSatisfiesPzs()

Output only. Reserved for future use.

bool satisfies_pzs = 17 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type Description
ModelMonitor.Builder

This builder for chaining.

clearTabularObjective()

public ModelMonitor.Builder clearTabularObjective()

Optional default tabular model monitoring objective.

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

Returns
Type Description
ModelMonitor.Builder

clearTrainingDataset()

public ModelMonitor.Builder clearTrainingDataset()

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
ModelMonitor.Builder

clearUpdateTime()

public ModelMonitor.Builder clearUpdateTime()

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
ModelMonitor.Builder

clone()

public ModelMonitor.Builder clone()
Returns
Type Description
ModelMonitor.Builder
Overrides

getCreateTime()

public 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.

getCreateTimeBuilder()

public Timestamp.Builder getCreateTimeBuilder()

Output only. Timestamp when this ModelMonitor was created.

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

Returns
Type Description
Builder

getCreateTimeOrBuilder()

public 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

getDefaultInstanceForType()

public ModelMonitor getDefaultInstanceForType()
Returns
Type Description
ModelMonitor

getDefaultObjectiveCase()

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

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()
Returns
Type Description
Descriptor
Overrides

getDisplayName()

public 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 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 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.

getExplanationSpecBuilder()

public ExplanationSpec.Builder getExplanationSpecBuilder()

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

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

Returns
Type Description
ExplanationSpec.Builder

getExplanationSpecOrBuilder()

public 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 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.

getModelMonitoringSchemaBuilder()

public ModelMonitoringSchema.Builder getModelMonitoringSchemaBuilder()

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.Builder

getModelMonitoringSchemaOrBuilder()

public 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 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.

getModelMonitoringTargetBuilder()

public ModelMonitor.ModelMonitoringTarget.Builder getModelMonitoringTargetBuilder()

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.Builder

getModelMonitoringTargetOrBuilder()

public 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 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 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 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.

getNotificationSpecBuilder()

public ModelMonitoringNotificationSpec.Builder getNotificationSpecBuilder()

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.Builder

getNotificationSpecOrBuilder()

public 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 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.

getOutputSpecBuilder()

public ModelMonitoringOutputSpec.Builder getOutputSpecBuilder()

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.Builder

getOutputSpecOrBuilder()

public 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

getSatisfiesPzi()

public boolean getSatisfiesPzi()

Output only. Reserved for future use.

bool satisfies_pzi = 18 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type Description
boolean

The satisfiesPzi.

getSatisfiesPzs()

public boolean getSatisfiesPzs()

Output only. Reserved for future use.

bool satisfies_pzs = 17 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type Description
boolean

The satisfiesPzs.

getTabularObjective()

public 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.

getTabularObjectiveBuilder()

public ModelMonitoringObjectiveSpec.TabularObjective.Builder getTabularObjectiveBuilder()

Optional default tabular model monitoring objective.

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

Returns
Type Description
ModelMonitoringObjectiveSpec.TabularObjective.Builder

getTabularObjectiveOrBuilder()

public 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 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.

getTrainingDatasetBuilder()

public ModelMonitoringInput.Builder getTrainingDatasetBuilder()

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.Builder

getTrainingDatasetOrBuilder()

public 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 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.

getUpdateTimeBuilder()

public Timestamp.Builder getUpdateTimeBuilder()

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
Builder

getUpdateTimeOrBuilder()

public 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 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 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 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 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 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 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 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 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 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.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
Type Description
FieldAccessorTable
Overrides

isInitialized()

public final boolean isInitialized()
Returns
Type Description
boolean
Overrides

mergeCreateTime(Timestamp value)

public ModelMonitor.Builder mergeCreateTime(Timestamp value)

Output only. Timestamp when this ModelMonitor was created.

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

Parameter
Name Description
value Timestamp
Returns
Type Description
ModelMonitor.Builder

mergeExplanationSpec(ExplanationSpec value)

public ModelMonitor.Builder mergeExplanationSpec(ExplanationSpec value)

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

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

Parameter
Name Description
value ExplanationSpec
Returns
Type Description
ModelMonitor.Builder

mergeFrom(ModelMonitor other)

public ModelMonitor.Builder mergeFrom(ModelMonitor other)
Parameter
Name Description
other ModelMonitor
Returns
Type Description
ModelMonitor.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public ModelMonitor.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
input CodedInputStream
extensionRegistry ExtensionRegistryLite
Returns
Type Description
ModelMonitor.Builder
Overrides
Exceptions
Type Description
IOException

mergeFrom(Message other)

public ModelMonitor.Builder mergeFrom(Message other)
Parameter
Name Description
other Message
Returns
Type Description
ModelMonitor.Builder
Overrides

mergeModelMonitoringSchema(ModelMonitoringSchema value)

public ModelMonitor.Builder mergeModelMonitoringSchema(ModelMonitoringSchema value)

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;

Parameter
Name Description
value ModelMonitoringSchema
Returns
Type Description
ModelMonitor.Builder

mergeModelMonitoringTarget(ModelMonitor.ModelMonitoringTarget value)

public ModelMonitor.Builder mergeModelMonitoringTarget(ModelMonitor.ModelMonitoringTarget value)

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;

Parameter
Name Description
value ModelMonitor.ModelMonitoringTarget
Returns
Type Description
ModelMonitor.Builder

mergeNotificationSpec(ModelMonitoringNotificationSpec value)

public ModelMonitor.Builder mergeNotificationSpec(ModelMonitoringNotificationSpec value)

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

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

Parameter
Name Description
value ModelMonitoringNotificationSpec
Returns
Type Description
ModelMonitor.Builder

mergeOutputSpec(ModelMonitoringOutputSpec value)

public ModelMonitor.Builder mergeOutputSpec(ModelMonitoringOutputSpec value)

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;

Parameter
Name Description
value ModelMonitoringOutputSpec
Returns
Type Description
ModelMonitor.Builder

mergeTabularObjective(ModelMonitoringObjectiveSpec.TabularObjective value)

public ModelMonitor.Builder mergeTabularObjective(ModelMonitoringObjectiveSpec.TabularObjective value)

Optional default tabular model monitoring objective.

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

Parameter
Name Description
value ModelMonitoringObjectiveSpec.TabularObjective
Returns
Type Description
ModelMonitor.Builder

mergeTrainingDataset(ModelMonitoringInput value)

public ModelMonitor.Builder mergeTrainingDataset(ModelMonitoringInput value)

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;

Parameter
Name Description
value ModelMonitoringInput
Returns
Type Description
ModelMonitor.Builder

mergeUnknownFields(UnknownFieldSet unknownFields)

public final ModelMonitor.Builder mergeUnknownFields(UnknownFieldSet unknownFields)
Parameter
Name Description
unknownFields UnknownFieldSet
Returns
Type Description
ModelMonitor.Builder
Overrides

mergeUpdateTime(Timestamp value)

public ModelMonitor.Builder mergeUpdateTime(Timestamp value)

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

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

Parameter
Name Description
value Timestamp
Returns
Type Description
ModelMonitor.Builder

setCreateTime(Timestamp value)

public ModelMonitor.Builder setCreateTime(Timestamp value)

Output only. Timestamp when this ModelMonitor was created.

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

Parameter
Name Description
value Timestamp
Returns
Type Description
ModelMonitor.Builder

setCreateTime(Timestamp.Builder builderForValue)

public ModelMonitor.Builder setCreateTime(Timestamp.Builder builderForValue)

Output only. Timestamp when this ModelMonitor was created.

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

Parameter
Name Description
builderForValue Builder
Returns
Type Description
ModelMonitor.Builder

setDisplayName(String value)

public ModelMonitor.Builder setDisplayName(String value)

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;

Parameter
Name Description
value String

The displayName to set.
Returns
Type Description
ModelMonitor.Builder

This builder for chaining.

setDisplayNameBytes(ByteString value)

public ModelMonitor.Builder setDisplayNameBytes(ByteString value)

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;

Parameter
Name Description
value ByteString

The bytes for displayName to set.
Returns
Type Description
ModelMonitor.Builder

This builder for chaining.

setExplanationSpec(ExplanationSpec value)

public ModelMonitor.Builder setExplanationSpec(ExplanationSpec value)

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

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

Parameter
Name Description
value ExplanationSpec
Returns
Type Description
ModelMonitor.Builder

setExplanationSpec(ExplanationSpec.Builder builderForValue)

public ModelMonitor.Builder setExplanationSpec(ExplanationSpec.Builder builderForValue)

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

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

Parameter
Name Description
builderForValue ExplanationSpec.Builder
Returns
Type Description
ModelMonitor.Builder

setField(Descriptors.FieldDescriptor field, Object value)

public ModelMonitor.Builder setField(Descriptors.FieldDescriptor field, Object value)
Parameters
Name Description
field FieldDescriptor
value Object
Returns
Type Description
ModelMonitor.Builder
Overrides

setModelMonitoringSchema(ModelMonitoringSchema value)

public ModelMonitor.Builder setModelMonitoringSchema(ModelMonitoringSchema value)

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;

Parameter
Name Description
value ModelMonitoringSchema
Returns
Type Description
ModelMonitor.Builder

setModelMonitoringSchema(ModelMonitoringSchema.Builder builderForValue)

public ModelMonitor.Builder setModelMonitoringSchema(ModelMonitoringSchema.Builder builderForValue)

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;

Parameter
Name Description
builderForValue ModelMonitoringSchema.Builder
Returns
Type Description
ModelMonitor.Builder

setModelMonitoringTarget(ModelMonitor.ModelMonitoringTarget value)

public ModelMonitor.Builder setModelMonitoringTarget(ModelMonitor.ModelMonitoringTarget value)

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;

Parameter
Name Description
value ModelMonitor.ModelMonitoringTarget
Returns
Type Description
ModelMonitor.Builder

setModelMonitoringTarget(ModelMonitor.ModelMonitoringTarget.Builder builderForValue)

public ModelMonitor.Builder setModelMonitoringTarget(ModelMonitor.ModelMonitoringTarget.Builder builderForValue)

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;

Parameter
Name Description
builderForValue ModelMonitor.ModelMonitoringTarget.Builder
Returns
Type Description
ModelMonitor.Builder

setName(String value)

public ModelMonitor.Builder setName(String value)

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

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

Parameter
Name Description
value String

The name to set.
Returns
Type Description
ModelMonitor.Builder

This builder for chaining.

setNameBytes(ByteString value)

public ModelMonitor.Builder setNameBytes(ByteString value)

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

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

Parameter
Name Description
value ByteString

The bytes for name to set.
Returns
Type Description
ModelMonitor.Builder

This builder for chaining.

setNotificationSpec(ModelMonitoringNotificationSpec value)

public ModelMonitor.Builder setNotificationSpec(ModelMonitoringNotificationSpec value)

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

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

Parameter
Name Description
value ModelMonitoringNotificationSpec
Returns
Type Description
ModelMonitor.Builder

setNotificationSpec(ModelMonitoringNotificationSpec.Builder builderForValue)

public ModelMonitor.Builder setNotificationSpec(ModelMonitoringNotificationSpec.Builder builderForValue)

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

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

Parameter
Name Description
builderForValue ModelMonitoringNotificationSpec.Builder
Returns
Type Description
ModelMonitor.Builder

setOutputSpec(ModelMonitoringOutputSpec value)

public ModelMonitor.Builder setOutputSpec(ModelMonitoringOutputSpec value)

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;

Parameter
Name Description
value ModelMonitoringOutputSpec
Returns
Type Description
ModelMonitor.Builder

setOutputSpec(ModelMonitoringOutputSpec.Builder builderForValue)

public ModelMonitor.Builder setOutputSpec(ModelMonitoringOutputSpec.Builder builderForValue)

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;

Parameter
Name Description
builderForValue ModelMonitoringOutputSpec.Builder
Returns
Type Description
ModelMonitor.Builder

setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

public ModelMonitor.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
Parameters
Name Description
field FieldDescriptor
index int
value Object
Returns
Type Description
ModelMonitor.Builder
Overrides

setSatisfiesPzi(boolean value)

public ModelMonitor.Builder setSatisfiesPzi(boolean value)

Output only. Reserved for future use.

bool satisfies_pzi = 18 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name Description
value boolean

The satisfiesPzi to set.
Returns
Type Description
ModelMonitor.Builder

This builder for chaining.

setSatisfiesPzs(boolean value)

public ModelMonitor.Builder setSatisfiesPzs(boolean value)

Output only. Reserved for future use.

bool satisfies_pzs = 17 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name Description
value boolean

The satisfiesPzs to set.
Returns
Type Description
ModelMonitor.Builder

This builder for chaining.

setTabularObjective(ModelMonitoringObjectiveSpec.TabularObjective value)

public ModelMonitor.Builder setTabularObjective(ModelMonitoringObjectiveSpec.TabularObjective value)

Optional default tabular model monitoring objective.

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

Parameter
Name Description
value ModelMonitoringObjectiveSpec.TabularObjective
Returns
Type Description
ModelMonitor.Builder

setTabularObjective(ModelMonitoringObjectiveSpec.TabularObjective.Builder builderForValue)

public ModelMonitor.Builder setTabularObjective(ModelMonitoringObjectiveSpec.TabularObjective.Builder builderForValue)

Optional default tabular model monitoring objective.

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

Parameter
Name Description
builderForValue ModelMonitoringObjectiveSpec.TabularObjective.Builder
Returns
Type Description
ModelMonitor.Builder

setTrainingDataset(ModelMonitoringInput value)

public ModelMonitor.Builder setTrainingDataset(ModelMonitoringInput value)

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;

Parameter
Name Description
value ModelMonitoringInput
Returns
Type Description
ModelMonitor.Builder

setTrainingDataset(ModelMonitoringInput.Builder builderForValue)

public ModelMonitor.Builder setTrainingDataset(ModelMonitoringInput.Builder builderForValue)

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;

Parameter
Name Description
builderForValue ModelMonitoringInput.Builder
Returns
Type Description
ModelMonitor.Builder

setUnknownFields(UnknownFieldSet unknownFields)

public final ModelMonitor.Builder setUnknownFields(UnknownFieldSet unknownFields)
Parameter
Name Description
unknownFields UnknownFieldSet
Returns
Type Description
ModelMonitor.Builder
Overrides

setUpdateTime(Timestamp value)

public ModelMonitor.Builder setUpdateTime(Timestamp value)

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

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

Parameter
Name Description
value Timestamp
Returns
Type Description
ModelMonitor.Builder

setUpdateTime(Timestamp.Builder builderForValue)

public ModelMonitor.Builder setUpdateTime(Timestamp.Builder builderForValue)

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

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

Parameter
Name Description
builderForValue Builder
Returns
Type Description
ModelMonitor.Builder