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
Inherited Members
com.google.protobuf.GeneratedMessageV3.<ListT>makeMutableCopy(ListT)
com.google.protobuf.GeneratedMessageV3.<ListT>makeMutableCopy(ListT,int)
com.google.protobuf.GeneratedMessageV3.<T>emptyList(java.lang.Class<T>)
com.google.protobuf.GeneratedMessageV3.internalGetMapFieldReflection(int)
Static Fields
public static final int CREATE_TIME_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int DISPLAY_NAME_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int ENCRYPTION_SPEC_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int EXPLANATION_SPEC_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int MODEL_MONITORING_SCHEMA_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int MODEL_MONITORING_TARGET_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int NAME_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int NOTIFICATION_SPEC_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int OUTPUT_SPEC_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int SATISFIES_PZI_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int SATISFIES_PZS_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int TABULAR_OBJECTIVE_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int TRAINING_DATASET_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int UPDATE_TIME_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
Static Methods
public static ModelMonitor getDefaultInstance()
public static final Descriptors.Descriptor getDescriptor()
public static ModelMonitor.Builder newBuilder()
public static ModelMonitor.Builder newBuilder(ModelMonitor prototype)
public static ModelMonitor parseDelimitedFrom(InputStream input)
public static ModelMonitor parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
public static ModelMonitor parseFrom(byte[] data)
| Parameter |
| Name |
Description |
data |
byte[]
|
public static ModelMonitor parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
public static ModelMonitor parseFrom(ByteString data)
public static ModelMonitor parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
public static ModelMonitor parseFrom(CodedInputStream input)
public static ModelMonitor parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
public static ModelMonitor parseFrom(InputStream input)
public static ModelMonitor parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
public static ModelMonitor parseFrom(ByteBuffer data)
public static ModelMonitor parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
public static Parser<ModelMonitor> parser()
Methods
public boolean equals(Object obj)
| Parameter |
| Name |
Description |
obj |
Object
|
Overrides
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.
|
public TimestampOrBuilder getCreateTimeOrBuilder()
Output only. Timestamp when this ModelMonitor was created.
.google.protobuf.Timestamp create_time = 6 [(.google.api.field_behavior) = OUTPUT_ONLY];
public ModelMonitor getDefaultInstanceForType()
public ModelMonitor.DefaultObjectiveCase getDefaultObjectiveCase()
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.
|
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.
|
public EncryptionSpec getEncryptionSpec()
Customer-managed encryption key spec for a ModelMonitor. If
set, this ModelMonitor and all sub-resources of this
ModelMonitor will be secured by this key.
.google.cloud.aiplatform.v1beta1.EncryptionSpec encryption_spec = 5;
public EncryptionSpecOrBuilder getEncryptionSpecOrBuilder()
Customer-managed encryption key spec for a ModelMonitor. If
set, this ModelMonitor and all sub-resources of this
ModelMonitor will be secured by this key.
.google.cloud.aiplatform.v1beta1.EncryptionSpec encryption_spec = 5;
public ExplanationSpec getExplanationSpec()
Optional model explanation spec. It is used for feature attribution
monitoring.
.google.cloud.aiplatform.v1beta1.ExplanationSpec explanation_spec = 16;
public ExplanationSpecOrBuilder getExplanationSpecOrBuilder()
Optional model explanation spec. It is used for feature attribution
monitoring.
.google.cloud.aiplatform.v1beta1.ExplanationSpec explanation_spec = 16;
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;
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;
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;
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;
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.
|
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.
|
public ModelMonitoringNotificationSpec getNotificationSpec()
Optional default notification spec, it can be overridden in the
ModelMonitoringJob notification spec.
.google.cloud.aiplatform.v1beta1.ModelMonitoringNotificationSpec notification_spec = 12;
public ModelMonitoringNotificationSpecOrBuilder getNotificationSpecOrBuilder()
Optional default notification spec, it can be overridden in the
ModelMonitoringJob notification spec.
.google.cloud.aiplatform.v1beta1.ModelMonitoringNotificationSpec notification_spec = 12;
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;
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;
public Parser<ModelMonitor> getParserForType()
Overrides
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.
|
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.
|
public int getSerializedSize()
| Returns |
| Type |
Description |
int |
|
Overrides
public ModelMonitoringObjectiveSpec.TabularObjective getTabularObjective()
Optional default tabular model monitoring objective.
.google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveSpec.TabularObjective tabular_objective = 11;
public ModelMonitoringObjectiveSpec.TabularObjectiveOrBuilder getTabularObjectiveOrBuilder()
Optional default tabular model monitoring objective.
.google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveSpec.TabularObjective tabular_objective = 11;
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;
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;
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.
|
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];
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.
|
public boolean hasEncryptionSpec()
Customer-managed encryption key spec for a ModelMonitor. If
set, this ModelMonitor and all sub-resources of this
ModelMonitor will be secured by this key.
.google.cloud.aiplatform.v1beta1.EncryptionSpec encryption_spec = 5;
| Returns |
| Type |
Description |
boolean |
Whether the encryptionSpec field is set.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
| Returns |
| Type |
Description |
int |
|
Overrides
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Overrides
public final boolean isInitialized()
Overrides
public ModelMonitor.Builder newBuilderForType()
protected ModelMonitor.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Overrides
protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
| Returns |
| Type |
Description |
Object |
|
Overrides
public ModelMonitor.Builder toBuilder()
public void writeTo(CodedOutputStream output)
Overrides
