a by comparing two datasets with identical schema.Target Drift reports and tests: helps detect and explore changes in the target function and/or model predictions by comparing two datasets where the target and/or prediction columns are available.

Regression Performance or Classification Performance reports and tests: evaluate the performance of a model by analyzing a single dataset where both the target and prediction columns are available. It can also compare it to the past performance of the same model, or the performance of an alternative model by providing a second dataset.

You should consider one of the other Data Validator flavors if you need a different set of data validation features.

How do you deploy it?

The Evidently Data Validator flavor is included in the Evidently ZenML integration, you need to install it on your local machine to be able to register an Evidently Data Validator and add it to your stack:

zenml integration install evidently -y

The Data Validator stack component does not have any configuration parameters. Adding it to a stack is as simple as running e.g.:

# Register the Evidently data validator

zenml data-validator register evidently_data_validator --flavor=evidently

# Register and set a stack with the new data validator

zenml stack register custom_stack -dv evidently_data_validator ... --set

How do you use it?

Data Profiling

Evidently's profiling functions take in a pandas.DataFrame dataset or a pair of datasets and generate results in the form of a Report object.