Concept | Checks#
Important
In Dataiku versions 12.6 and above, data quality rules have replaced checks on datasets. Checks on folders, models, and model evaluation stores covered in this article are available in all versions of Dataiku.
Checks monitor the measurements, or metrics, on certain Flow objects — managed folders, saved models, or model evaluation stores.
Metrics are measurements on the object, such as the size of a folder or the accuracy of a model. Checks use the latest metric measurement to monitor the status of the item.
Check examples#
For instance, we could use checks to verify that:
The size of a folder does not exceed 3GB.
The model accuracy does not fall below 0.8.

Check outputs#
Checks will return one of four outputs:
Output |
Meaning |
---|---|
OK |
The rule outcome satisfied the set condition. |
Error |
The rule condition is not respected or the computation itself failed. |
Warning |
The rule fails a soft condition but not a hard one. |
Empty |
The rule cannot be computed. |
Configuring checks#
You can configure checks in the Settings tab of saved models and model evaluation stores or in the Status tab of managed folders.

See also
You can create custom checks with Python code. To learn more, get started with Concept | Custom metrics, checks, data quality rules & scenarios.