Create monitoring jobs via the UI¶
In addition to the Prediction API, you can create monitoring job definitions through the DataRobot UI. You can then view and manage monitoring job definitions as you would any other job definition.
To create the monitoring jobs in DataRobot:
-
Click Deployments and select a deployment from the inventory.
-
On the selected deployment's Overview, click Job Definitions.
-
On the Job Definitions page, click Monitoring Jobs, and then click Add Job Definition.
-
On the New Monitoring Job Definition page, configure the following options:
Field name Description 
Monitoring job definition name Enter the name of the monitoring job that you are creating for the deployment. 
Monitoring data source Set the source type and define the connection for the data to be scored. 
Monitoring options Configure monitoring options and aggregation options. 
Data destination (Optional) Configure the data destination options if you enable output monitoring. 
Jobs schedule Configure whether to run the job immediately and whether to schedule the job. 
Save monitoring job definition Click this button to save the job definition. The button changes to Save and run monitoring job definition if Run this job immediately is enabled. Note that this button is disabled if there are any validation errors.
Set monitoring data source¶
Select a monitoring source, called an intake adapter, and complete the appropriate authentication workflow for the source type. Select a connection type below to view field descriptions:
Note
When browsing for connections, invalid adapters are not shown.
Database connections
Cloud Storage Connections
Data Warehouse Connections
Other
After you set your monitoring source, DataRobot validates that the data is applicable to the deployed model.
Note
DataRobot validates that a data source is compatible with the model when possible, but not in all cases. DataRobot validates for AI Catalog, most JDBC connections, Snowflake, and Synapse.
Set monitoring options¶
When setting the monitoring options, the options available depend on the model type: regression or classification.
| Option | Description |
|---|---|
| Association ID column | Identifies the column in the data source containing the association ID for predictions. |
| Predictions column | Identifies the column in the data source containing prediction values. You must provide this field and/or Actuals value column. |
| Actuals value column | Identifies the column in the data source containing actual values. You must provide this field and/or Predictions column. |
| Actuals timestamp column | Identifies the column in the data source containing the timestamps for actual values. |
| Option | Description |
|---|---|
| Association ID column | Identifies the column in the data source containing the association ID for predictions. |
| Predictions column | Identifies the columns in the data source containing each prediction class. You must provide this field and/or Actuals value column. |
| Actuals value column | Identifies the column in the data source containing actual values. You must provide this field and/or Predictions column. |
| Actuals timestamp column | Identifies the column in the data source containing the timestamps for actual values. |
Set aggregation options¶
For external models with large-scale monitoring enabled, you can enable the Use aggregation option and define the retention policy and the amount of raw data retained for challengers. To support the use of challenger models, you must send raw features. For large datasets, you can report a small sample of raw feature and prediction data to support challengers and reporting; then, you can send the remaining data in aggregate format.
| Property | Description |
|---|---|
| Retention policy | The policy definition determines if the Retention value represents a number of Samples or a Percentage of the dataset. |
| Retention value | The amount of data to retain, either a percentage of data or number of samples. |
Important
If you define these properties, raw data is aggregated by the MLOps library. This means that the data isn't stored in the DataRobot platform. Stats aggregation only supports feature and prediction data, not actuals data. If you've defined one or more of the Association ID column, Actuals value column, or Actuals timestamp column, DataRobot cannot aggregate data. If you enable the Use aggregation option, the association ID and actuals-related fields are disabled.
Set output monitoring and data destination options¶
After setting the prediction and actuals monitoring options, you can choose to enable Output monitoring status and configure the following options:
| Option | Description |
|---|---|
| Monitored status column | Identifies the column in the data destination containing the monitoring status for each row. |
| Unique row identifier columns | Identifies the columns from the data source to serve as unique identifiers for each row. These columns are copied to the data destination to associate each monitored status with its corresponding source row. |
With Output monitoring status enabled, you must also configure the Data destination options to specify where the monitored data results should be stored. Select a monitoring data destination, called an output adapter, and complete the appropriate authentication workflow for the destination type. Select a connection type below to view field descriptions:
Note
When browsing for connections, invalid adapters are not shown.
Database connections
Cloud Storage Connections
Data Warehouse Connections
Other
Schedule monitoring jobs¶
You can schedule monitoring jobs to run automatically on a schedule. When outlining a monitoring job definition, enable Run this job automatically on a schedule, then specify the frequency (daily, hourly, monthly, etc.) and time of day to define the schedule on which the job runs.
For further granularity, select Use advanced scheduler. You can set the exact time (to the minute) you want to run the monitoring job.
After setting all applicable options, click Save monitoring job definition.