Struct ModelDeploymentMonitoringJob 

#[non_exhaustive]
pub struct ModelDeploymentMonitoringJob {

    pub name: String,
    pub display_name: String,
    pub endpoint: String,
    pub state: JobState,
    pub schedule_state: MonitoringScheduleState,
    pub latest_monitoring_pipeline_metadata: Option<LatestMonitoringPipelineMetadata>,
    pub model_deployment_monitoring_objective_configs: Vec<ModelDeploymentMonitoringObjectiveConfig>,
    pub model_deployment_monitoring_schedule_config: Option<ModelDeploymentMonitoringScheduleConfig>,
    pub logging_sampling_strategy: Option<SamplingStrategy>,
    pub model_monitoring_alert_config: Option<ModelMonitoringAlertConfig>,
    pub predict_instance_schema_uri: String,
    pub sample_predict_instance: Option<Value>,
    pub analysis_instance_schema_uri: String,
    pub bigquery_tables: Vec<ModelDeploymentMonitoringBigQueryTable>,
    pub log_ttl: Option<Duration>,
    pub labels: HashMap<String, String>,
    pub create_time: Option<Timestamp>,
    pub update_time: Option<Timestamp>,
    pub next_schedule_time: Option<Timestamp>,
    pub stats_anomalies_base_directory: Option<GcsDestination>,
    pub encryption_spec: Option<EncryptionSpec>,
    pub enable_monitoring_pipeline_logs: bool,
    pub error: Option<Status>,
    pub satisfies_pzs: bool,
    pub satisfies_pzi: bool,
    /* private fields */
}

Available on crate feature job-service only.

Represents a job that runs periodically to monitor the deployed models in an endpoint. It will analyze the logged training & prediction data to detect any abnormal behaviors.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

name: String

Output only. Resource name of a ModelDeploymentMonitoringJob.

display_name: String

Required. The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.

endpoint: String

Required. Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

state: JobState

Output only. The detailed state of the monitoring job. When the job is still creating, the state will be ‘PENDING’. Once the job is successfully created, the state will be ‘RUNNING’. Pause the job, the state will be ‘PAUSED’. Resume the job, the state will return to ‘RUNNING’.

schedule_state: MonitoringScheduleState

Output only. Schedule state when the monitoring job is in Running state.

latest_monitoring_pipeline_metadata: Option<LatestMonitoringPipelineMetadata>

Output only. Latest triggered monitoring pipeline metadata.

model_deployment_monitoring_objective_configs: Vec<ModelDeploymentMonitoringObjectiveConfig>

Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.

model_deployment_monitoring_schedule_config: Option<ModelDeploymentMonitoringScheduleConfig>

Required. Schedule config for running the monitoring job.

logging_sampling_strategy: Option<SamplingStrategy>

Required. Sample Strategy for logging.

model_monitoring_alert_config: Option<ModelMonitoringAlertConfig>

Alert config for model monitoring.

predict_instance_schema_uri: String

YAML schema file uri describing the format of a single instance, which are given to format this Endpoint’s prediction (and explanation). If not set, we will generate predict schema from collected predict requests.

sample_predict_instance: Option<Value>

Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests.

analysis_instance_schema_uri: String

YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze.

If this field is empty, all the feature data types are inferred from predict_instance_schema_uri, meaning that TFDV will use the data in the exact format(data type) as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.

bigquery_tables: Vec<ModelDeploymentMonitoringBigQueryTable>

Output only. The created bigquery tables for the job under customer project. Customer could do their own query & analysis. There could be 4 log tables in maximum:

1. Training data logging predict request/response
2. Serving data logging predict request/response

log_ttl: Option<Duration>

The TTL of BigQuery tables in user projects which stores logs. A day is the basic unit of the TTL and we take the ceil of TTL/86400(a day). e.g. { second: 3600} indicates ttl = 1 day.

labels: HashMap<String, String>

The labels with user-defined metadata to organize your ModelDeploymentMonitoringJob.

Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed.

See https://goo.gl/xmQnxf for more information and examples of labels.

create_time: Option<Timestamp>

Output only. Timestamp when this ModelDeploymentMonitoringJob was created.

update_time: Option<Timestamp>

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

next_schedule_time: Option<Timestamp>

Output only. Timestamp when this monitoring pipeline will be scheduled to run for the next round.

stats_anomalies_base_directory: Option<GcsDestination>

Stats anomalies base folder path.

encryption_spec: Option<EncryptionSpec>

Customer-managed encryption key spec for a ModelDeploymentMonitoringJob. If set, this ModelDeploymentMonitoringJob and all sub-resources of this ModelDeploymentMonitoringJob will be secured by this key.

enable_monitoring_pipeline_logs: bool

If true, the scheduled monitoring pipeline logs are sent to Google Cloud Logging, including pipeline status and anomalies detected. Please note the logs incur cost, which are subject to Cloud Logging pricing.

error: Option<Status>

Output only. Only populated when the job’s state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

satisfies_pzs: bool

Output only. Reserved for future use.

satisfies_pzi: bool

Output only. Reserved for future use.

Implementations

impl ModelDeploymentMonitoringJob

pub fn new() -> Self

pub fn set_name<T: Into<String>>(self, v: T) -> Self

Sets the value of name.

Example

let x = ModelDeploymentMonitoringJob::new().set_name("example");

pub fn set_display_name<T: Into<String>>(self, v: T) -> Self

Sets the value of display_name.

Example

let x = ModelDeploymentMonitoringJob::new().set_display_name("example");

pub fn set_endpoint<T: Into<String>>(self, v: T) -> Self

Sets the value of endpoint.

Example

let x = ModelDeploymentMonitoringJob::new().set_endpoint("example");

pub fn set_state<T: Into<JobState>>(self, v: T) -> Self

Sets the value of state.

Example

use google_cloud_aiplatform_v1::model::JobState;
let x0 = ModelDeploymentMonitoringJob::new().set_state(JobState::Queued);
let x1 = ModelDeploymentMonitoringJob::new().set_state(JobState::Pending);
let x2 = ModelDeploymentMonitoringJob::new().set_state(JobState::Running);

pub fn set_schedule_state<T: Into<MonitoringScheduleState>>(self, v: T) -> Self

Sets the value of schedule_state.

Example

use google_cloud_aiplatform_v1::model::model_deployment_monitoring_job::MonitoringScheduleState;
let x0 = ModelDeploymentMonitoringJob::new().set_schedule_state(MonitoringScheduleState::Pending);
let x1 = ModelDeploymentMonitoringJob::new().set_schedule_state(MonitoringScheduleState::Offline);
let x2 = ModelDeploymentMonitoringJob::new().set_schedule_state(MonitoringScheduleState::Running);

pub fn set_latest_monitoring_pipeline_metadata<T>(self, v: T) -> Self

where T: Into<LatestMonitoringPipelineMetadata>,

Sets the value of latest_monitoring_pipeline_metadata.

Example

use google_cloud_aiplatform_v1::model::model_deployment_monitoring_job::LatestMonitoringPipelineMetadata;
let x = ModelDeploymentMonitoringJob::new().set_latest_monitoring_pipeline_metadata(LatestMonitoringPipelineMetadata::default()/* use setters */);

pub fn set_or_clear_latest_monitoring_pipeline_metadata<T>( self, v: Option<T>, ) -> Self

where T: Into<LatestMonitoringPipelineMetadata>,

Sets or clears the value of latest_monitoring_pipeline_metadata.

Example

use google_cloud_aiplatform_v1::model::model_deployment_monitoring_job::LatestMonitoringPipelineMetadata;
let x = ModelDeploymentMonitoringJob::new().set_or_clear_latest_monitoring_pipeline_metadata(Some(LatestMonitoringPipelineMetadata::default()/* use setters */));
let x = ModelDeploymentMonitoringJob::new().set_or_clear_latest_monitoring_pipeline_metadata(None::<LatestMonitoringPipelineMetadata>);

pub fn set_model_deployment_monitoring_objective_configs<T, V>( self, v: T, ) -> Self

where T: IntoIterator<Item = V>, V: Into<ModelDeploymentMonitoringObjectiveConfig>,

Sets the value of model_deployment_monitoring_objective_configs.

Example

use google_cloud_aiplatform_v1::model::ModelDeploymentMonitoringObjectiveConfig;
let x = ModelDeploymentMonitoringJob::new()
    .set_model_deployment_monitoring_objective_configs([
        ModelDeploymentMonitoringObjectiveConfig::default()/* use setters */,
        ModelDeploymentMonitoringObjectiveConfig::default()/* use (different) setters */,
    ]);

pub fn set_model_deployment_monitoring_schedule_config<T>(self, v: T) -> Self

where T: Into<ModelDeploymentMonitoringScheduleConfig>,

Sets the value of model_deployment_monitoring_schedule_config.

Example

use google_cloud_aiplatform_v1::model::ModelDeploymentMonitoringScheduleConfig;
let x = ModelDeploymentMonitoringJob::new().set_model_deployment_monitoring_schedule_config(ModelDeploymentMonitoringScheduleConfig::default()/* use setters */);

pub fn set_or_clear_model_deployment_monitoring_schedule_config<T>( self, v: Option<T>, ) -> Self

where T: Into<ModelDeploymentMonitoringScheduleConfig>,

Sets or clears the value of model_deployment_monitoring_schedule_config.

Example

use google_cloud_aiplatform_v1::model::ModelDeploymentMonitoringScheduleConfig;
let x = ModelDeploymentMonitoringJob::new().set_or_clear_model_deployment_monitoring_schedule_config(Some(ModelDeploymentMonitoringScheduleConfig::default()/* use setters */));
let x = ModelDeploymentMonitoringJob::new().set_or_clear_model_deployment_monitoring_schedule_config(None::<ModelDeploymentMonitoringScheduleConfig>);

pub fn set_logging_sampling_strategy<T>(self, v: T) -> Self

where T: Into<SamplingStrategy>,

Sets the value of logging_sampling_strategy.

Example

use google_cloud_aiplatform_v1::model::SamplingStrategy;
let x = ModelDeploymentMonitoringJob::new().set_logging_sampling_strategy(SamplingStrategy::default()/* use setters */);

pub fn set_or_clear_logging_sampling_strategy<T>(self, v: Option<T>) -> Self

where T: Into<SamplingStrategy>,

Sets or clears the value of logging_sampling_strategy.

Example

use google_cloud_aiplatform_v1::model::SamplingStrategy;
let x = ModelDeploymentMonitoringJob::new().set_or_clear_logging_sampling_strategy(Some(SamplingStrategy::default()/* use setters */));
let x = ModelDeploymentMonitoringJob::new().set_or_clear_logging_sampling_strategy(None::<SamplingStrategy>);

pub fn set_model_monitoring_alert_config<T>(self, v: T) -> Self

where T: Into<ModelMonitoringAlertConfig>,

Sets the value of model_monitoring_alert_config.

Example

use google_cloud_aiplatform_v1::model::ModelMonitoringAlertConfig;
let x = ModelDeploymentMonitoringJob::new().set_model_monitoring_alert_config(ModelMonitoringAlertConfig::default()/* use setters */);

pub fn set_or_clear_model_monitoring_alert_config<T>(self, v: Option<T>) -> Self

where T: Into<ModelMonitoringAlertConfig>,

Sets or clears the value of model_monitoring_alert_config.

Example

use google_cloud_aiplatform_v1::model::ModelMonitoringAlertConfig;
let x = ModelDeploymentMonitoringJob::new().set_or_clear_model_monitoring_alert_config(Some(ModelMonitoringAlertConfig::default()/* use setters */));
let x = ModelDeploymentMonitoringJob::new().set_or_clear_model_monitoring_alert_config(None::<ModelMonitoringAlertConfig>);

pub fn set_predict_instance_schema_uri<T: Into<String>>(self, v: T) -> Self

Sets the value of predict_instance_schema_uri.

Example

let x = ModelDeploymentMonitoringJob::new().set_predict_instance_schema_uri("example");

pub fn set_sample_predict_instance<T>(self, v: T) -> Self

where T: Into<Value>,

Sets the value of sample_predict_instance.

Example

use wkt::Value;
let x = ModelDeploymentMonitoringJob::new().set_sample_predict_instance(Value::default()/* use setters */);

pub fn set_or_clear_sample_predict_instance<T>(self, v: Option<T>) -> Self

where T: Into<Value>,

Sets or clears the value of sample_predict_instance.

Example

use wkt::Value;
let x = ModelDeploymentMonitoringJob::new().set_or_clear_sample_predict_instance(Some(Value::default()/* use setters */));
let x = ModelDeploymentMonitoringJob::new().set_or_clear_sample_predict_instance(None::<Value>);

pub fn set_analysis_instance_schema_uri<T: Into<String>>(self, v: T) -> Self

Sets the value of analysis_instance_schema_uri.

Example

let x = ModelDeploymentMonitoringJob::new().set_analysis_instance_schema_uri("example");

pub fn set_bigquery_tables<T, V>(self, v: T) -> Self

where T: IntoIterator<Item = V>, V: Into<ModelDeploymentMonitoringBigQueryTable>,

Sets the value of bigquery_tables.

Example

use google_cloud_aiplatform_v1::model::ModelDeploymentMonitoringBigQueryTable;
let x = ModelDeploymentMonitoringJob::new()
    .set_bigquery_tables([
        ModelDeploymentMonitoringBigQueryTable::default()/* use setters */,
        ModelDeploymentMonitoringBigQueryTable::default()/* use (different) setters */,
    ]);

pub fn set_log_ttl<T>(self, v: T) -> Self

where T: Into<Duration>,

Sets the value of log_ttl.

Example

use wkt::Duration;
let x = ModelDeploymentMonitoringJob::new().set_log_ttl(Duration::default()/* use setters */);

pub fn set_or_clear_log_ttl<T>(self, v: Option<T>) -> Self

where T: Into<Duration>,

Sets or clears the value of log_ttl.

Example

use wkt::Duration;
let x = ModelDeploymentMonitoringJob::new().set_or_clear_log_ttl(Some(Duration::default()/* use setters */));
let x = ModelDeploymentMonitoringJob::new().set_or_clear_log_ttl(None::<Duration>);

pub fn set_labels<T, K, V>(self, v: T) -> Self

where T: IntoIterator<Item = (K, V)>, K: Into<String>, V: Into<String>,

Sets the value of labels.

Example

let x = ModelDeploymentMonitoringJob::new().set_labels([
    ("key0", "abc"),
    ("key1", "xyz"),
]);

pub fn set_create_time<T>(self, v: T) -> Self

where T: Into<Timestamp>,

Sets the value of create_time.

Example

use wkt::Timestamp;
let x = ModelDeploymentMonitoringJob::new().set_create_time(Timestamp::default()/* use setters */);

pub fn set_or_clear_create_time<T>(self, v: Option<T>) -> Self

where T: Into<Timestamp>,

Sets or clears the value of create_time.

Example

use wkt::Timestamp;
let x = ModelDeploymentMonitoringJob::new().set_or_clear_create_time(Some(Timestamp::default()/* use setters */));
let x = ModelDeploymentMonitoringJob::new().set_or_clear_create_time(None::<Timestamp>);

pub fn set_update_time<T>(self, v: T) -> Self

where T: Into<Timestamp>,

Sets the value of update_time.

Example

use wkt::Timestamp;
let x = ModelDeploymentMonitoringJob::new().set_update_time(Timestamp::default()/* use setters */);

pub fn set_or_clear_update_time<T>(self, v: Option<T>) -> Self

where T: Into<Timestamp>,

Sets or clears the value of update_time.

Example

use wkt::Timestamp;
let x = ModelDeploymentMonitoringJob::new().set_or_clear_update_time(Some(Timestamp::default()/* use setters */));
let x = ModelDeploymentMonitoringJob::new().set_or_clear_update_time(None::<Timestamp>);

pub fn set_next_schedule_time<T>(self, v: T) -> Self

where T: Into<Timestamp>,

Sets the value of next_schedule_time.

Example

use wkt::Timestamp;
let x = ModelDeploymentMonitoringJob::new().set_next_schedule_time(Timestamp::default()/* use setters */);

pub fn set_or_clear_next_schedule_time<T>(self, v: Option<T>) -> Self

where T: Into<Timestamp>,

Sets or clears the value of next_schedule_time.

Example

use wkt::Timestamp;
let x = ModelDeploymentMonitoringJob::new().set_or_clear_next_schedule_time(Some(Timestamp::default()/* use setters */));
let x = ModelDeploymentMonitoringJob::new().set_or_clear_next_schedule_time(None::<Timestamp>);

pub fn set_stats_anomalies_base_directory<T>(self, v: T) -> Self

where T: Into<GcsDestination>,

Sets the value of stats_anomalies_base_directory.

Example

use google_cloud_aiplatform_v1::model::GcsDestination;
let x = ModelDeploymentMonitoringJob::new().set_stats_anomalies_base_directory(GcsDestination::default()/* use setters */);

pub fn set_or_clear_stats_anomalies_base_directory<T>( self, v: Option<T>, ) -> Self

where T: Into<GcsDestination>,

Sets or clears the value of stats_anomalies_base_directory.

Example

use google_cloud_aiplatform_v1::model::GcsDestination;
let x = ModelDeploymentMonitoringJob::new().set_or_clear_stats_anomalies_base_directory(Some(GcsDestination::default()/* use setters */));
let x = ModelDeploymentMonitoringJob::new().set_or_clear_stats_anomalies_base_directory(None::<GcsDestination>);

pub fn set_encryption_spec<T>(self, v: T) -> Self

where T: Into<EncryptionSpec>,

Sets the value of encryption_spec.

Example

use google_cloud_aiplatform_v1::model::EncryptionSpec;
let x = ModelDeploymentMonitoringJob::new().set_encryption_spec(EncryptionSpec::default()/* use setters */);

pub fn set_or_clear_encryption_spec<T>(self, v: Option<T>) -> Self

where T: Into<EncryptionSpec>,

Sets or clears the value of encryption_spec.

Example

use google_cloud_aiplatform_v1::model::EncryptionSpec;
let x = ModelDeploymentMonitoringJob::new().set_or_clear_encryption_spec(Some(EncryptionSpec::default()/* use setters */));
let x = ModelDeploymentMonitoringJob::new().set_or_clear_encryption_spec(None::<EncryptionSpec>);

pub fn set_enable_monitoring_pipeline_logs<T: Into<bool>>(self, v: T) -> Self

Sets the value of enable_monitoring_pipeline_logs.

Example

let x = ModelDeploymentMonitoringJob::new().set_enable_monitoring_pipeline_logs(true);

pub fn set_error<T>(self, v: T) -> Self

where T: Into<Status>,

Sets the value of error.

Example

use rpc::model::Status;
let x = ModelDeploymentMonitoringJob::new().set_error(Status::default()/* use setters */);

pub fn set_or_clear_error<T>(self, v: Option<T>) -> Self

where T: Into<Status>,

Sets or clears the value of error.

Example

use rpc::model::Status;
let x = ModelDeploymentMonitoringJob::new().set_or_clear_error(Some(Status::default()/* use setters */));
let x = ModelDeploymentMonitoringJob::new().set_or_clear_error(None::<Status>);

pub fn set_satisfies_pzs<T: Into<bool>>(self, v: T) -> Self

Sets the value of satisfies_pzs.

Example

let x = ModelDeploymentMonitoringJob::new().set_satisfies_pzs(true);

pub fn set_satisfies_pzi<T: Into<bool>>(self, v: T) -> Self

Sets the value of satisfies_pzi.

Example

let x = ModelDeploymentMonitoringJob::new().set_satisfies_pzi(true);

Trait Implementations

impl Clone for ModelDeploymentMonitoringJob

fn clone(&self) -> ModelDeploymentMonitoringJob

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for ModelDeploymentMonitoringJob

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

Formats the value using the given formatter.

impl Default for ModelDeploymentMonitoringJob

fn default() -> ModelDeploymentMonitoringJob

Returns the “default value” for a type.

impl Message for ModelDeploymentMonitoringJob

fn typename() -> &'static str

The typename of this message.

impl PartialEq for ModelDeploymentMonitoringJob

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

Tests for self and other values to be equal, and is used by ==.

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

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

impl StructuralPartialEq for ModelDeploymentMonitoringJob