Struct google_ml1::api::GoogleCloudMlV1__RequestLoggingConfig[][src]

pub struct GoogleCloudMlV1__RequestLoggingConfig {
    pub bigquery_table_name: Option<String>,
    pub sampling_percentage: Option<f64>,
}

Configuration for logging request-response pairs to a BigQuery table. Online prediction requests to a model version and the responses to these requests are converted to raw strings and saved to the specified BigQuery table. Logging is constrained by BigQuery quotas and limits. If your project exceeds BigQuery quotas or limits, AI Platform Prediction does not log request-response pairs, but it continues to serve predictions. If you are using continuous evaluation, you do not need to specify this configuration manually. Setting up continuous evaluation automatically enables logging of request-response pairs.

This type is not used in any activity, and only used as part of another schema.

Fields

bigquery_table_name: Option<String>

Required. Fully qualified BigQuery table name in the following format: “ project_id.dataset_name.table_name“ The specified table must already exist, and the “Cloud ML Service Agent” for your project must have permission to write to it. The table must have the following schema: Field nameType Mode model STRING REQUIRED model_version STRING REQUIRED time TIMESTAMP REQUIRED raw_data STRING REQUIRED raw_prediction STRING NULLABLE groundtruth STRING NULLABLE

sampling_percentage: Option<f64>

Percentage of requests to be logged, expressed as a fraction from 0 to 1. For example, if you want to log 10% of requests, enter 0.1. The sampling window is the lifetime of the model version. Defaults to 0.

Trait Implementations

impl Clone for GoogleCloudMlV1__RequestLoggingConfig[src]

impl Debug for GoogleCloudMlV1__RequestLoggingConfig[src]

impl Default for GoogleCloudMlV1__RequestLoggingConfig[src]

impl<'de> Deserialize<'de> for GoogleCloudMlV1__RequestLoggingConfig[src]

impl Part for GoogleCloudMlV1__RequestLoggingConfig[src]

impl Serialize for GoogleCloudMlV1__RequestLoggingConfig[src]

Auto Trait Implementations

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