Set up data drift monitoring¶

When deploying a model, there is a chance that the dataset used for training and validation differs from the prediction data. You can enable data drift monitoring on the Data Drift > Settings tab. DataRobot monitors both target and feature drift information and displays results on the Data Drift tab.

On a deployment's Data Drift Settings page, you can configure the following settings:

Define data drift monitoring notifications¶

Drift assesses how the distribution of data changes across all features for a specified range. The thresholds you set determine the amount of drift you will allow before a notification is triggered.

Use the Definition section of the Data Drift > Settings tab to set thresholds for drift and importance:

• Drift is a measure of how new prediction data differs from the original data used to train the model.

• Importance allows you to separate the features you care most about from those that are less important.

For both drift and importance, you can visualize the thresholds and how they separate the features on the Data Drift tab. By default, the data drift status for deployments is marked as "Failing" () when at least one high-importance feature exceeds the set drift metric threshold; it is marked as "At Risk" () when no high-importance features, but at least one low-importance feature exceeds the threshold.

Deployment Owners can customize the rules used to calculate the drift status for each deployment. As a deployment Owner, you can:

• Define or override the list of high or low-importance features to monitor features that are important to you or put less emphasis on less important features.

• Exclude features expected to drift from drift status calculation and alerting so you do not get false alarms.

• Customize what "At Risk" and "Failing" drift statuses mean to personalize and tailor the drift status of each deployment to your needs.

To set up monitoring of drift status for a deployment:

1. On the Data Drift Settings page, in the Definition section, configure the settings for monitoring data drift:

   

   	Element	Description
   1	Range	Adjusts the time range of the Reference period, which compares training data to prediction data. Select a time range from the dropdown menu.
   2	Drift metric and threshold	Configures the thresholds of the drift metric. DataRobot only supports the Population Stability Index (PSI) metric. When drift thresholds are changed, the Feature Drift vs. Feature Importance chart updates to reflect the changes. For more information, see the note on Drift metric support below.
   3	Importance metric and threshold	Configures the thresholds of the Importance metric. The Importance metric measures the most impactful features in the training data. DataRobot only supports the Permutation Importance metric. When drift thresholds are changed, the Feature Drift vs. Feature Importance chart updates to reflect the changes. See an example.
   4	X excluded features	Excludes features (including the target) from drift status calculations. Click X excluded features to open a dialog box where you can enter the names of features to set as Drift exclusions. Excluded features do not affect drift status for the deployment but still display on the Feature Drift vs. Feature Importance chart. See an example.
   5	X starred features	Sets features to be treated as high importance even if they were initially assigned low importance. Click X starred features to open a dialog box where you can enter the names of features to set as High-importance stars. Once added, these features are assigned high importance. They ignore the importance thresholds, but still display on the Feature Drift vs. Feature Importance chart. See an example.
   6	"At Risk" / "Failing" thresholds	Configures the values that trigger drift statuses for "At Risk" () and "Failing" (). See an example.

   Note

   Changes to thresholds affect the periods of time in which predictions are made across the entire history of a deployment. These updated thresholds are reflected in the performance monitoring visualizations on the Data Drift tab.

2. After updating the data drift monitoring settings, click Save.

Example of an excluded feature¶

In the example below, the excluded feature, which appears as a gray circle, would normally change the drift status to "Failing" (). Because it is excluded, the status remains as Passing.

Example of configuring the importance and drift thresholds¶

In the example below, the chart has adjusted the importance and drift thresholds (indicated by the arrows), resulting in more features "At Risk" and "Failing" than the chart above.

Example of starring a feature to assign high importance¶

In the example below, the starred feature, which appears as a white circle, would normally cause drift status to be "At Risk" due to its initially low importance. However, since it is assigned high importance, the feature will change the drift status to "Failing" ().

Example of setting a drift status rule¶

The following example configures the rule for a deployment to mark its drift status as "At Risk" if one of the following is true:

• The number of low-importance features above the drift threshold is greater than 1.

• The number of high-importance features above the drift threshold is greater than 3.