Class ModelMonitor (3.50.0)

public final class ModelMonitor extends GeneratedMessageV3 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

Static Fields

CREATE_TIME_FIELD_NUMBER

public static final int CREATE_TIME_FIELD_NUMBER
Field Value
Type Description
int

DISPLAY_NAME_FIELD_NUMBER

public static final int DISPLAY_NAME_FIELD_NUMBER
Field Value
Type Description
int

EXPLANATION_SPEC_FIELD_NUMBER

public static final int EXPLANATION_SPEC_FIELD_NUMBER
Field Value
Type Description
int

MODEL_MONITORING_SCHEMA_FIELD_NUMBER

public static final int MODEL_MONITORING_SCHEMA_FIELD_NUMBER
Field Value
Type Description
int

MODEL_MONITORING_TARGET_FIELD_NUMBER

public static final int MODEL_MONITORING_TARGET_FIELD_NUMBER
Field Value
Type Description
int

NAME_FIELD_NUMBER

public static final int NAME_FIELD_NUMBER
Field Value
Type Description
int

NOTIFICATION_SPEC_FIELD_NUMBER

public static final int NOTIFICATION_SPEC_FIELD_NUMBER
Field Value
Type Description
int

OUTPUT_SPEC_FIELD_NUMBER

public static final int OUTPUT_SPEC_FIELD_NUMBER
Field Value
Type Description
int

SATISFIES_PZI_FIELD_NUMBER

public static final int SATISFIES_PZI_FIELD_NUMBER
Field Value
Type Description
int

SATISFIES_PZS_FIELD_NUMBER

public static final int SATISFIES_PZS_FIELD_NUMBER
Field Value
Type Description
int

TABULAR_OBJECTIVE_FIELD_NUMBER

public static final int TABULAR_OBJECTIVE_FIELD_NUMBER
Field Value
Type Description
int

TRAINING_DATASET_FIELD_NUMBER

public static final int TRAINING_DATASET_FIELD_NUMBER
Field Value
Type Description
int

UPDATE_TIME_FIELD_NUMBER

public static final int UPDATE_TIME_FIELD_NUMBER
Field Value
Type Description
int

Static Methods

getDefaultInstance()

public static ModelMonitor getDefaultInstance()
Returns
Type Description
ModelMonitor

getDescriptor()

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

newBuilder()

public static ModelMonitor.Builder newBuilder()
Returns
Type Description
ModelMonitor.Builder

newBuilder(ModelMonitor prototype)

public static ModelMonitor.Builder newBuilder(ModelMonitor prototype)
Parameter
Name Description
prototype ModelMonitor
Returns
Type Description
ModelMonitor.Builder

parseDelimitedFrom(InputStream input)

public static ModelMonitor parseDelimitedFrom(InputStream input)
Parameter
Name Description
input InputStream
Returns
Type Description
ModelMonitor
Exceptions
Type Description
IOException

parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static ModelMonitor parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
input InputStream
extensionRegistry ExtensionRegistryLite
Returns
Type Description
ModelMonitor
Exceptions
Type Description
IOException

parseFrom(byte[] data)

public static ModelMonitor parseFrom(byte[] data)
Parameter
Name Description
data byte[]
Returns
Type Description
ModelMonitor
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)

public static ModelMonitor parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
data byte[]
extensionRegistry ExtensionRegistryLite
Returns
Type Description
ModelMonitor
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(ByteString data)

public static ModelMonitor parseFrom(ByteString data)
Parameter
Name Description
data ByteString
Returns
Type Description
ModelMonitor
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)

public static ModelMonitor parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
data ByteString
extensionRegistry ExtensionRegistryLite
Returns
Type Description
ModelMonitor
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(CodedInputStream input)

public static ModelMonitor parseFrom(CodedInputStream input)
Parameter
Name Description
input CodedInputStream
Returns
Type Description
ModelMonitor
Exceptions
Type Description
IOException

parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public static ModelMonitor parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
input CodedInputStream
extensionRegistry ExtensionRegistryLite
Returns
Type Description
ModelMonitor
Exceptions
Type Description
IOException

parseFrom(InputStream input)

public static ModelMonitor parseFrom(InputStream input)
Parameter
Name Description
input InputStream
Returns
Type Description
ModelMonitor
Exceptions
Type Description
IOException

parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static ModelMonitor parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
input InputStream
extensionRegistry ExtensionRegistryLite
Returns
Type Description
ModelMonitor
Exceptions
Type Description
IOException

parseFrom(ByteBuffer data)

public static ModelMonitor parseFrom(ByteBuffer data)
Parameter
Name Description
data ByteBuffer
Returns
Type Description
ModelMonitor
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)

public static ModelMonitor parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
data ByteBuffer
extensionRegistry ExtensionRegistryLite
Returns
Type Description
ModelMonitor
Exceptions
Type Description
InvalidProtocolBufferException

parser()

public static Parser<ModelMonitor> parser()
Returns
Type Description
Parser<ModelMonitor>

Methods

equals(Object obj)

public boolean equals(Object obj)
Parameter
Name Description
obj Object
Returns
Type Description
boolean
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.

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

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.

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.

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.

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.

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.

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

getParserForType()

public Parser<ModelMonitor> getParserForType()
Returns
Type Description
Parser<ModelMonitor>
Overrides

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.

getSerializedSize()

public int getSerializedSize()
Returns
Type Description
int
Overrides

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.

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.

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.

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.

hashCode()

public int hashCode()
Returns
Type Description
int
Overrides

internalGetFieldAccessorTable()

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

isInitialized()

public final boolean isInitialized()
Returns
Type Description
boolean
Overrides

newBuilderForType()

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

newBuilderForType(GeneratedMessageV3.BuilderParent parent)

protected ModelMonitor.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Parameter
Name Description
parent BuilderParent
Returns
Type Description
ModelMonitor.Builder
Overrides

newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)

protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
Parameter
Name Description
unused UnusedPrivateParameter
Returns
Type Description
Object
Overrides

toBuilder()

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

writeTo(CodedOutputStream output)

public void writeTo(CodedOutputStream output)
Parameter
Name Description
output CodedOutputStream
Overrides
Exceptions
Type Description
IOException