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 Settings > Data drift tab. DataRobot monitors both target and feature drift information and displays results on the Monitoring > Data drift tab.
How does DataRobot track drift?
For data drift, DataRobot tracks:
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Target drift: DataRobot stores statistics about predictions to monitor how the distribution and values of the target change over time. As a baseline for comparing target distributions, DataRobot uses the distribution of predictions on the holdout.
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Feature drift: DataRobot stores statistics about predictions to monitor how distributions and values of features change over time. As a baseline for comparing distributions of features:
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For training datasets larger than 500MB, DataRobot uses the distribution of a random sample of the training data.
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For training datasets smaller than 500MB, DataRobot uses the distribution of 100% of the training data.
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On a deployment's Data Drift Settings page, configure the following settings:
| Field | Description |
|---|---|
| Data Drift | |
| Enable feature drift tracking | Configures DataRobot to track feature drift in a deployment. Training data is required for feature drift tracking. |
| Enable target monitoring | Configures DataRobot to track target drift in a deployment. Target monitoring is required for accuracy monitoring. |
| Training data | |
| Training data | Displays the dataset used as a training baseline while building a model. |
| Inference data | |
| DataRobot is storing your predictions | Confirms DataRobot is recording and storing the results of any predictions made by this deployment. DataRobot stores a deployment's inference data when a deployment is created. It cannot be uploaded separately. |
| Inference data (external model) | |
| DataRobot is recording the results of any predictions made against this deployment | Confirms DataRobot is recording and storing the results of any predictions made by the external model. |
| Drop file(s) here or choose file | Uploads a file with prediction history data to monitor data drift. |
| Definition | |
| Set definition | Configures the drift and importance metric settings and threshold definitions for data drift monitoring. |
Availability information
Data drift tracking is only available for deployments using deployment-aware prediction API routes (i.e., https://example.datarobot.com/predApi/v1.0/deployments/<deploymentId>/predictions).
Data privacy notice
DataRobot monitors both target and feature drift information by default and displays results on the Monitoring > Data drift tab. Use the Enable target monitoring and Enable feature drift tracking toggles to turn off tracking if, for example, you have sensitive data that should not be monitored in the deployment. The Enable target monitoring setting is also required to enable accuracy monitoring.
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.
Drift monitoring configuration permissions
Only deployment Owners can modify data drift monitoring settings; however, Users can configure the conditions under which notifications are sent to them. Consumers cannot modify monitoring or notification settings.
In addition, deployment Owners can customize the rules (or definitions) used to calculate the drift status for each deployment. As a deployment Owner, you can:
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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.
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Exclude features expected to drift from drift status calculation and alerting so you do not get false alarms.
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Customize what "At Risk" and "Failing" drift statuses mean to personalize and tailor the drift status of each deployment to your needs.
Use the Definition section of the Settings > Data drift tab to set thresholds for drift and importance:
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Drift is a measure of how new prediction data differs from the original data used to train the model.
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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.
To set up drift status monitoring for a deployment, 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 | DataRobot only supports the Population Stability Index (PSI) metric. For more information, see the note on Drift metric support below. |
| 3 | Importance metric | DataRobot only supports the Permutation Importance metric. The importance metric measures the most impactful features in the training data. |
| 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. |
| 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. |
| 6 | Drift threshold | Configures the thresholds of the drift metric. When drift thresholds are changed, the Feature Drift vs. Feature Importance chart updates to reflect the changes. |
| 7 | Importance threshold | Configures the thresholds of the importance metric. The importance metric measures the most impactful features in the training data. When drift thresholds are changed, the Feature Drift vs. Feature Importance chart updates to reflect the changes. |
| 8 | "At Risk" / "Failing" thresholds | Configures the values that trigger drift statuses for "At Risk" () and "Failing" (). |
Note
Changes to thresholds affect the periods 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.
Drift metric support
While the DataRobot UI only supports the Population Stability Index (PSI) metric, the DataRobot API supports Kullback-Leibler Divergence, Hellinger Distance, Histogram Intersection, and Jensen–Shannon Divergence. In addition, using the Python API client, you can retrieve a list of supported metrics.