Struct google_api_proto::google::cloud::aiplatform::v1beta1::ModelMonitor

pub struct ModelMonitor {

    pub name: String,
    pub display_name: String,
    pub model_monitoring_target: Option<ModelMonitoringTarget>,
    pub training_dataset: Option<ModelMonitoringInput>,
    pub notification_spec: Option<ModelMonitoringNotificationSpec>,
    pub output_spec: Option<ModelMonitoringOutputSpec>,
    pub explanation_spec: Option<ExplanationSpec>,
    pub model_monitoring_schema: Option<ModelMonitoringSchema>,
    pub create_time: Option<Timestamp>,
    pub update_time: Option<Timestamp>,
    pub satisfies_pzs: bool,
    pub satisfies_pzi: bool,
    pub default_objective: Option<DefaultObjective>,
}

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.

Fields

name: String

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

display_name: String

The display name of the ModelMonitor. The name can be up to 128 characters long and can consist of any UTF-8.

model_monitoring_target: Option<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.

training_dataset: Option<ModelMonitoringInput>

Optional training dataset used to train the model. It can serve as a reference dataset to identify changes in production.

notification_spec: Option<ModelMonitoringNotificationSpec>

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

output_spec: Option<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.

explanation_spec: Option<ExplanationSpec>

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

model_monitoring_schema: Option<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.

create_time: Option<Timestamp>

Output only. Timestamp when this ModelMonitor was created.

update_time: Option<Timestamp>

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

satisfies_pzs: bool

Output only. Reserved for future use.

satisfies_pzi: bool

Output only. Reserved for future use.

default_objective: Option<DefaultObjective>

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

Trait Implementations

impl Clone for ModelMonitor

fn clone(&self) -> ModelMonitor

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for ModelMonitor

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for ModelMonitor

fn default() -> Self

Returns the “default value” for a type.

impl Message for ModelMonitor

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode(&self, buf: &mut impl BufMut) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

fn encode_length_delimited( &self, buf: &mut impl BufMut, ) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message with a length-delimiter to a buffer.

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message with a length-delimiter to a newly allocated buffer.

fn decode(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes a length-delimited instance of the message from the buffer.

fn merge(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes an instance of the message from a buffer, and merges it into self.

fn merge_length_delimited(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq for ModelMonitor

fn eq(&self, other: &ModelMonitor) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for ModelMonitor