g tests to your Evidently test step configuration:to add a single test or test preset: call EvidentlyTestConfig.test with an Evidently test or test preset class name (or class path or class). The rest of the parameters are the same ones that you would usually pass to the Evidently test or test preset class constructor.

to generate multiple tests, similar to calling the Evidently column test generator: call EvidentlyTestConfig.test_generator with an Evidently test or test preset class name (or class path or class) and a list of column names. The rest of the parameters are the same ones that you would usually pass to the Evidently test or test preset class constructor.

The ZenML Evidently test step can then be inserted into your pipeline where it can take in two datasets and outputs the Evidently test suite results generated in both JSON and HTML formats, e.g.:

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

def text_data_test_pipeline():

"""Links all the steps together in a pipeline."""

data = data_loader()

reference_dataset, comparison_dataset = data_splitter(data)

json_report, html_report = text_data_test(

reference_dataset=reference_dataset,

comparison_dataset=comparison_dataset,

text_data_test_pipeline()

For a version of the same step that works with a single dataset, simply don't pass any comparison dataset:

text_data_test(reference_dataset=reference_dataset)

You should consult the official Evidently documentation for more information on what each test is useful for and what data columns it requires as input.

The evidently_test_step step also allows for additional Test options to be passed to the TestSuite constructor e.g.:

from zenml.integrations.evidently.steps import (

EvidentlyColumnMapping,

text_data_test = evidently_test_step.with_options(

parameters=dict(

test_options = [

"evidently.options.ColorOptions", {

"primary_color": "#5a86ad",

"fill_color": "#fff4f2",

"zero_line_color": "#016795",

"current_data_color": "#c292a1",

"reference_data_color": "#017b92",