Struct GoogleCloudAiplatformV1ModelDeploymentMonitoringJob

pub struct GoogleCloudAiplatformV1ModelDeploymentMonitoringJob {

    pub encryption_spec: Option<GoogleCloudAiplatformV1EncryptionSpec>,
    pub create_time: Option<DateTime<Utc>>,
    pub latest_monitoring_pipeline_metadata: Option<GoogleCloudAiplatformV1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadata>,
    pub model_deployment_monitoring_schedule_config: Option<GoogleCloudAiplatformV1ModelDeploymentMonitoringScheduleConfig>,
    pub display_name: Option<String>,
    pub sample_predict_instance: Option<Value>,
    pub model_monitoring_alert_config: Option<GoogleCloudAiplatformV1ModelMonitoringAlertConfig>,
    pub log_ttl: Option<Duration>,
    pub next_schedule_time: Option<DateTime<Utc>>,
    pub error: Option<GoogleRpcStatus>,
    pub analysis_instance_schema_uri: Option<String>,
    pub labels: Option<HashMap<String, String>>,
    pub enable_monitoring_pipeline_logs: Option<bool>,
    pub stats_anomalies_base_directory: Option<GoogleCloudAiplatformV1GcsDestination>,
    pub schedule_state: Option<String>,
    pub update_time: Option<DateTime<Utc>>,
    pub bigquery_tables: Option<Vec<GoogleCloudAiplatformV1ModelDeploymentMonitoringBigQueryTable>>,
    pub predict_instance_schema_uri: Option<String>,
    pub logging_sampling_strategy: Option<GoogleCloudAiplatformV1SamplingStrategy>,
    pub model_deployment_monitoring_objective_configs: Option<Vec<GoogleCloudAiplatformV1ModelDeploymentMonitoringObjectiveConfig>>,
    pub endpoint: Option<String>,
    pub name: Option<String>,
    pub state: Option<String>,
}

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.

Activities

This type is used in activities, which are methods you may call on this type or where this type is involved in. The list links the activity name, along with information about where it is used (one of request and response).

• locations model deployment monitoring jobs patch projects (request)
• locations model deployment monitoring jobs get projects (response)
• locations model deployment monitoring jobs create projects (request|response)

Fields

encryption_spec: Option<GoogleCloudAiplatformV1EncryptionSpec>

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.

create_time: Option<DateTime<Utc>>

Output only. Timestamp when this ModelDeploymentMonitoringJob was created.

latest_monitoring_pipeline_metadata: Option<GoogleCloudAiplatformV1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadata>

Output only. Latest triggered monitoring pipeline metadata.

model_deployment_monitoring_schedule_config: Option<GoogleCloudAiplatformV1ModelDeploymentMonitoringScheduleConfig>

Required. Schedule config for running the monitoring job.

display_name: Option<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.

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.

model_monitoring_alert_config: Option<GoogleCloudAiplatformV1ModelMonitoringAlertConfig>

Alert config for model monitoring.

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.

next_schedule_time: Option<DateTime<Utc>>

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

error: Option<GoogleRpcStatus>

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

analysis_instance_schema_uri: Option<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.

labels: Option<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.

enable_monitoring_pipeline_logs: Option<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.

stats_anomalies_base_directory: Option<GoogleCloudAiplatformV1GcsDestination>

Stats anomalies base folder path.

schedule_state: Option<String>

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

update_time: Option<DateTime<Utc>>

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

bigquery_tables: Option<Vec<GoogleCloudAiplatformV1ModelDeploymentMonitoringBigQueryTable>>

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

predict_instance_schema_uri: Option<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.

logging_sampling_strategy: Option<GoogleCloudAiplatformV1SamplingStrategy>

Required. Sample Strategy for logging.

model_deployment_monitoring_objective_configs: Option<Vec<GoogleCloudAiplatformV1ModelDeploymentMonitoringObjectiveConfig>>

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

endpoint: Option<String>

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

name: Option<String>

Output only. Resource name of a ModelDeploymentMonitoringJob.

state: Option<String>

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’.

Trait Implementations

impl Clone for GoogleCloudAiplatformV1ModelDeploymentMonitoringJob

fn clone(&self) -> GoogleCloudAiplatformV1ModelDeploymentMonitoringJob

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for GoogleCloudAiplatformV1ModelDeploymentMonitoringJob

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

Formats the value using the given formatter.

impl Default for GoogleCloudAiplatformV1ModelDeploymentMonitoringJob

fn default() -> GoogleCloudAiplatformV1ModelDeploymentMonitoringJob

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for GoogleCloudAiplatformV1ModelDeploymentMonitoringJob

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for GoogleCloudAiplatformV1ModelDeploymentMonitoringJob

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl RequestValue for GoogleCloudAiplatformV1ModelDeploymentMonitoringJob

impl ResponseResult for GoogleCloudAiplatformV1ModelDeploymentMonitoringJob