To start, we included the checks that make sense when you do not have true labels or ground truth yet.
Here are the presets that come with the first release: Data Quality.
This preset is focused on the data quality issues like duplicate rows or null values. It helps detect bad or corrupted data. Data Stability.
This test suite identifies the changes in the data or differences between the batches. For example, it detects the appearance of the new columns or values, the difference in the number of entries, or if features are far out of range.
While the previous preset relies on descriptive statistics, this one compares feature distributions using statistical tests and distance metrics
. By default, it uses the in-built Evidently drift detection logic
which selects the detection method based on data volume and type. You can also swap it for a test or metric of your choice. NoTargetPerformance.
It combines several checks you might want to run when you generate the model predictions, but you do not yet have the actuals or ground truth labels. This includes checking for prediction drift and some of the data quality and stability checks.
We will continue expanding the presets list and welcome community contributions! The checks focused on the Model Performance will come in the next update.