ModelMonitor class final
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.
- Inheritance
-
- Object
- ProtoMessage
- ModelMonitor
Constructors
- ModelMonitor({ModelMonitoringObjectiveSpec_TabularObjective? tabularObjective, String name = '', String displayName = '', ModelMonitor_ModelMonitoringTarget? modelMonitoringTarget, ModelMonitoringInput? trainingDataset, ModelMonitoringNotificationSpec? notificationSpec, ModelMonitoringOutputSpec? outputSpec, ExplanationSpec? explanationSpec, ModelMonitoringSchema? modelMonitoringSchema, EncryptionSpec? encryptionSpec, Timestamp? createTime, Timestamp? updateTime, bool satisfiesPzs = false, bool satisfiesPzi = false})
-
ModelMonitor.fromJson(Map<
String, dynamic> json) -
factory
Properties
- createTime → Timestamp?
-
Output only. Timestamp when this ModelMonitor was created.
final
- displayName → String
-
The display name of the ModelMonitor.
The name can be up to 128 characters long and can consist of any UTF-8.
final
- encryptionSpec → EncryptionSpec?
-
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.
final
- explanationSpec → ExplanationSpec?
-
Optional model explanation spec. It is used for feature attribution
monitoring.
final
- hashCode → int
-
The hash code for this object.
no setterinherited
- modelMonitoringSchema → ModelMonitoringSchema?
-
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.
final
- modelMonitoringTarget → ModelMonitor_ModelMonitoringTarget?
-
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.
final
- name → String
-
Immutable. Resource name of the ModelMonitor. Format:
projects/{project}/locations/{location}/modelMonitors/{model_monitor}.final - notificationSpec → ModelMonitoringNotificationSpec?
-
Optional default notification spec, it can be overridden in the
ModelMonitoringJob notification spec.
final
- outputSpec → ModelMonitoringOutputSpec?
-
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.
final
- qualifiedName → String
-
The fully qualified name of this message, i.e.,
google.protobuf.Durationorgoogle.rpc.ErrorInfo.finalinherited - runtimeType → Type
-
A representation of the runtime type of the object.
no setterinherited
- satisfiesPzi → bool
-
Output only. Reserved for future use.
final
- satisfiesPzs → bool
-
Output only. Reserved for future use.
final
- tabularObjective → ModelMonitoringObjectiveSpec_TabularObjective?
-
Optional default tabular model monitoring objective.
final
- trainingDataset → ModelMonitoringInput?
-
Optional training dataset used to train the model.
It can serve as a reference dataset to identify changes in production.
final
- updateTime → Timestamp?
-
Output only. Timestamp when this ModelMonitor was updated most recently.
final
Methods
-
noSuchMethod(
Invocation invocation) → dynamic -
Invoked when a nonexistent method or property is accessed.
inherited
-
toJson(
) → Object -
override
-
toString(
) → String -
A string representation of this object.
override
Operators
-
operator ==(
Object other) → bool -
The equality operator.
inherited
Constants
- fullyQualifiedName → const String