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public static final class ModelMonitor.Builder extends GeneratedMessageV3.Builder<ModelMonitor.Builder> implements ModelMonitorOrBuilderVertex 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.BuilderImplements
ModelMonitorOrBuilderStatic 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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
isInitialized()
public final boolean isInitialized()| Returns | |
|---|---|
| Type | Description |
boolean |
|
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 |
|
| Exceptions | |
|---|---|
| Type | Description |
IOException |
|
mergeFrom(Message other)
public ModelMonitor.Builder mergeFrom(Message other)| Parameter | |
|---|---|
| Name | Description |
other |
Message |
| Returns | |
|---|---|
| Type | Description |
ModelMonitor.Builder |
|
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 |
|
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 |
StringThe 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 |
ByteStringThe 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 |
|
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 |
StringThe 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 |
ByteStringThe 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 |
|
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 |
booleanThe 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 |
booleanThe 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 |
|
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 |
|