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Target drift monitoring

Basic concepts: 

  • Baseline: Users can define the baseline basis on ‘Tag’ or segment of data based on ‘date’.
  • Frequency: Users can define how frequently they want to calculate the monitoring metrics
  • Alerts frequency: Users can configure how frequently they want to be notified about the alerts

Target Drift has similar inputs to 'Data Drift'. But unlike 'Data drift', you need to define the Baseline and Current data parameters, along with the True label, predicted label and the model type.

Through GUI

The dashboard report provides a detailed analysis of the target distribution by feature.

Drift Metrics: These statistical tests are available to analyze data drifts, namely the Chi-square test, Jensen-Shannon distance, Kolmogorov-Smirnov (K-S) test, Population Stability index (Psi), and Z-test.

You can learn more about these tests in our wiki section

Selecting dates: If you are selecting the dates, then the entire data under that tag will be used for calculating the drift. When you are selecting the date variable, ensure that there is data within these dates.

Mixing multiple tags: If you want to merge data from different tags, you can simply select multiple tags in the segment(baseline/current).

Alerts and Monitors

From here you can easily create and view customized alerts for Data drift, target drift and model performance through the alerts dashboard. For this, select ‘Create alerts' in the 'Monitors' tab and define the baseline and current data parameters like we did above and set the frequency of alerts, which can be daily, weekly, monthly, quarterly or yearly. 

To create new alerts, go to:

 ML Monitoring (Main menu on left) > select ‘Monitoring’ (from the sub-tabs) > click ‘Create Alerts’

All the newly created and existing alerts are displayed on this dashboard, along with details of trigger creator, name, type and options. 

Target Drift Monitor

You can not only track target drift but can get notified if there is an identified drift in your data. To set up a 'Target drift', select 'Target drift' under 'Monitor type', post which you can the specific details.

AryaXAI - Setting Target drift monitors
  • Select model type: Classification/Regression.
  • Select drift calculation metrics: You can choose drift calculation metrics from the list provided, and set the threshold for data drift and dataset drift (when the dataset itself was drifting).
  • Select the baseline and current: Use the tags to define the baseline and current. 'Current' is your production data if you are tracking drift in your production data.
  • Select Baseline/Current true label: Map the appropriate feature for 'Baseline true label' & 'Current true label'.
  • Segmenting the baseline or current: You can use date features to further segment your baseline. You can also 'Time period in days' to dynamically select the recent 'n' days as the current data. If you have added 'Time period in days', it'll use that value as the time period the day it calculated the drift as the end date.

Alert Report

The ‘Alert’ tab (beside the Monitoring sub-tab) displays the list of alerts that have been triggered. Clicking ‘View trigger info’ displays the Trigger details, such as the current data size, data drift triggered, drift percentage, etc.


If there is an identified drift, you'll get the alert for the same in both the web app and email at the specified frequency.

Web app alerts: Any alert triggered will be displayed as a notification on the top right corner. You can view all notifications from the tab and clear them. 

AryaXAI: Notifications

Email Alerts: The admin of the workspace will get an email if there is an identified drift.

Through SDK

To fetch the default target drift dashboard, use the following command:


If you need to create a new dashboard:


You can use the help function to get all parameters and payloads


Available Stat tests:

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