Detection Event Rules¶

This section outlines how to configure automation to receive notifications or start retraining after a Detection Event occurs.

When a Detection Event is produced, the ML cube Platform reviews all the Detection Event Rules you have set and triggers those matching the event.

Rules are specific to a task and are characterized by the following attributes:

Attribute	Description
Name	A descriptive label of the rule.
Detection Event Type	The type of event that triggers the rule.
Severity	The severity of the event that triggers the rule. It is only applicable to drift events. If not specified, the rule will be triggered by drift events of any severity.
Monitoring Target	The Monitoring Target whose event should trigger the rule.
Monitoring Metric	The Monitoring Metric whose event should trigger the rule.
Specification	The specification of the Monitoring Metric being monitored, if existing.
Model name	The name of the model to which the rule applies. This is only required when the monitoring target is related to a model (such as `ERROR` or `PREDICTION`).
Actions	A list of actions to be executed sequentially when the rule is triggered.
Segment ID	It refers to the ID of the Segment where the rule is set.

Detection Event Actions¶

Three types of actions are currently supported: notification, plot configuration and retrain.

Notifications¶

These actions send notifications to external services when a detection event is triggered. The following notification options are available:

Channel	Description
Slack Notification	Sends a notification to a Slack channel via webhook.
Discord Notification	Sends a notification to a Discord channel via webhook.
Email Notification	Sends an email to the provided email address.
Teams Notification	Sends a notification to Microsoft Teams via webhook.
Mqtt Notification	Sends a notification to an MQTT broker.

Plot Configuration¶

This action consists in creating two plot configurations when a Detection Event is triggered: the first one includes data preceding the event, while the second one includes data following the event.

Retrain¶

Retrain Action enables the automatic retraining of your model. Therefore, it is only available when the target of the rule is related to a model. The retrain action does not need any parameter because it is automatically inferred from the model name attribute of the rule. Of course, the model must already have a Retrain Trigger associated before setting up this action.

SDK Example

The following code demonstrates how to create a rule that matches high severity drift events on the error of a model. When triggered, it first sends a notification to the ml3-platform-notifications channel on your Slack workspace, using the provided webhook URL, and then starts the retraining of the model.

rule_id = client.create_detection_event_rule(
    name='Retrain model with notification',
    task_id='my-task-id',
    model_name='my-model',
    severity=DetectionEventSeverity.HIGH,
    detection_event_type=DetectionEventType.DRIFT_ON,
    monitoring_target=MonitoringTarget.ERROR,
    actions=[
        SlackNotificationAction(
            webhook='https://hooks.slack.com/services/...',
            channel='ml3-platform-notifications'
        ),
        RetrainAction()
    ],
)