e.g. feature drift, label drift, new labels etc.).Model Performance Checks for tabular or computer vision data: evaluate a model and detect problems with its performance (e.g. confusion matrix, boosting overfit, model error analysis)

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

zenml integration install deepchecks -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 Deepchecks data validator

zenml data-validator register deepchecks_data_validator --flavor=deepchecks

# Register and set a stack with the new data validator

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

How do you use it?

The ZenML integration restructures the way Deepchecks validation checks are organized in four categories, based on the type and number of input parameters that they expect as input. This makes it easier to reason about them when you decide which tests to use in your pipeline steps:

data integrity checks expect a single dataset as input. These correspond one-to-one to the set of Deepchecks data integrity checks for tabular and computer vision data

data drift checks require two datasets as input: target and reference. These correspond one-to-one to the set of Deepchecks train-test checks for tabular data and for computer vision.

model validation checks require a single dataset 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 a single dataset as input.