Detection Event¶

A Detection Event is raised by the ML cube Platform when a significant change is detected in one of the entities being monitored.

An event is characterized by the following attributes:

Attribute	Description
Eventy Type	The type of the event. It's possible values are: `Warning On`: the monitoring entity is experiencing slight changes that might lead to a drift. `Warning Off`: the monitoring entity has returned to the reference distribution. `Drift On`: the monitoring entity has drifted from the reference distribution. `Drift Off`: the monitoring entity has returned to the reference distribution.
Severity	The severity of the event. It's provided only for drift events and it can be `Low`, `Medium`, or `High`.
Monitoring Target	The Monitoring Target being monitored.
Monitoring Metric	The Monitoring Metric being monitored.
Specification	The specification of the Monitoring Metric being monitored, if existing.
Model Name	The name of the model that raised the event. It's present only if the event is related to a model.
Model Version	The version of the model that raised the event. It's present only if the event is related to a model.
Insert datetime	The time when the event was raised.
Sample timestamp	The timestamp of the sample that triggered the event.
Sample customer ID	The ID of the sample that triggered the event.
User feedback	The feedback provided by the user on whether the event was expected or not.
Segment ID	The ID of the Segment being monitored.

Retrieve Detection Events¶

You can access the detection events generated by the Platform in two ways:

SDK: it can be used to retrieve all detection events for a specific task programmatically.
WebApp: navigate to the Detection section located in the task page's sidebar. Here, all detection events are displayed in a table, with multiple filtering options available for useful event management. Additionally, the latest detection events identified are shown in the Task homepage, in the section named "Latest Detection Events".

SDK Example

The following code demonstrates how to retrieve all detection events for a specific task.

detection_events = client.get_detection_events(task_id='my-task-id')

User Feedback¶

When a Drift On event is raised, you can provide feedback on whether the event was expected or not. This feedback is then used to tune the monitoring algorithms and improve their performance. The feedback can be provided through the WebApp, in the Detection section of the task page, or through the SDK.

Detection Event Rules¶

To automate actions upon the reception of a detection event, you can set up Detection Event Rules. You can learn more about how to configure them in the Detection Event Rules section.