uter vision that expect a single dataset as input.model drift checks require two datasets and a mandatory model as input. This list includes a subset of the model evaluation checks provided by Deepchecks for tabular data and for computer vision that expect two datasets as input: target and reference.

This structure is directly reflected in how Deepchecks can be used with ZenML: there are four different Deepchecks standard steps and four different ZenML enums for Deepchecks checks . The Deepchecks Data Validator API is also modeled to reflect this same structure.

A notable characteristic of Deepchecks is that you don't need to customize the set of Deepchecks tests that are part of a test suite. Both ZenML and Deepchecks provide sane defaults that will run all available Deepchecks tests in a given category with their default conditions if a custom list of tests and conditions are not provided.

There are three ways you can use Deepchecks in your ZenML pipelines that allow different levels of flexibility:

instantiate, configure and insert one or more of the standard Deepchecks steps shipped with ZenML into your pipelines. This is the easiest way and the recommended approach, but can only be customized through the supported step configuration parameters.

call the data validation methods provided by the Deepchecks Data Validator in your custom step implementation. This method allows for more flexibility concerning what can happen in the pipeline step, but you are still limited to the functionality implemented in the Data Validator.

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

You can visualize Deepchecks results in Jupyter notebooks or view them directly in the ZenML dashboard.

Warning! Usage in remote orchestrators