.evidently.steps import (

EvidentlyColumnMapping,text_data_report = evidently_report_step.with_options(

parameters=dict(

report_options = [

"evidently.options.ColorOptions", {

"primary_color": "#5a86ad",

"fill_color": "#fff4f2",

"zero_line_color": "#016795",

"current_data_color": "#c292a1",

"reference_data_color": "#017b92",

),

],

You can view the complete list of configuration parameters in the SDK docs.

Data Validation

Aside from data profiling, Evidently can also be used to configure and run automated data validation tests on your data.

Similar to using Evidently through ZenML to run data profiling, there are three ways you can use Evidently to run data validation tests in your ZenML pipelines that allow different levels of flexibility:

instantiate, configure and insert the standard Evidently test step shipped with ZenML into your pipelines. This is the easiest way and the recommended approach.

call the data validation methods provided by the Evidently Data Validator in your custom step implementation. This method allows for more flexibility concerning what can happen in the pipeline step.

use the Evidently library directly in your custom step implementation. This gives you complete freedom in how you are using Evidently's features.

You can visualize Evidently reports in Jupyter notebooks or view them directly in the ZenML dashboard by clicking on the respective artifact in the pipeline run DAG.

You can visualize Evidently reports in Jupyter notebooks or view them directly in the ZenML dashboard by clicking on the respective artifact in the pipeline run DAG.

ZenML wraps the Evidently data validation functionality in the form of a standard Evidently test pipeline step that you can simply instantiate and insert in your pipeline. Here you can see how instantiating and configuring the standard Evidently test step can be done using our included evidently_test_step utility function:

from zenml.integrations.evidently.steps import (

EvidentlyColumnMapping,

evidently_test_step,