ts on two datasets as input: target and reference.DeepchecksModelValidationCheckStep: class DeepchecksModelDriftCheckStep(BaseStep): use it in your pipelines to run model performance tests using a single dataset and a mandatory model artifact as input

DeepchecksModelDriftCheckStep: use it in your pipelines to run model comparison/drift tests using a mandatory model artifact and two datasets as input: target and reference.

The integration doesn't yet include standard steps for computer vision, but you can still write your own custom steps that call the Deepchecks Data Validator API or even call the Deepchecks library directly.

All four standard steps behave similarly regarding the configuration parameters and returned artifacts, with the following differences:

the type and number of input artifacts are different, as mentioned above

each step expects a different enum data type to be used when explicitly listing the checks to be performed via the check_list configuration attribute. See the zenml.integrations.deepchecks.validation_checks module for more details about these enums (e.g. the data integrity step expects a list of DeepchecksDataIntegrityCheck values).

This section will only cover how you can use the data integrity step, with a similar usage to be easily inferred for the other three steps.

To instantiate a data integrity step that will run all available Deepchecks data integrity tests with their default configuration, e.g.:

from zenml.integrations.deepchecks.steps import (

deepchecks_data_integrity_check_step,

data_validator = deepchecks_data_integrity_check_step.with_options(

parameters=dict(

dataset_kwargs=dict(label="target", cat_features=[]),

),

The step can then be inserted into your pipeline where it can take in a dataset, e.g.:

docker_settings = DockerSettings(required_integrations=[DEEPCHECKS, SKLEARN])

@pipeline(settings={"docker": docker_settings})

def data_validation_pipeline():

df_train, df_test = data_loader()

data_validator(dataset=df_train)

data_validation_pipeline()