Whylogs

How to collect and visualize statistics to track changes in your pipelines' data with whylogs/WhyLabs profiling.

The whylogs/WhyLabs Data Validator flavor provided with the ZenML integration uses whylogs and WhyLabs to generate and track data profiles, highly accurate descriptive representations of your data. The profiles can be used to implement automated corrective actions in your pipelines, or to render interactive representations for further visual interpretation, evaluation and documentation.

When would you want to use it?

Whylogs is an open-source library that analyzes your data and creates statistical summaries called whylogs profiles. Whylogs profiles can be processed in your pipelines and visualized locally or uploaded to the WhyLabs platform, where more in depth analysis can be carried out. Even though whylogs also supports other data types, the ZenML whylogs integration currently only works with tabular data in pandas.DataFrame format.

You should use the whylogs/WhyLabs Data Validator when you need the following data validation features that are possible with whylogs and WhyLabs:

Data Quality: validate data quality in model inputs or in a data pipeline

Data Drift: detect data drift in model input features

Model Drift: Detect training-serving skew, concept drift, and model performance degradation

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

zenml integration install whylogs -y

If you don't need to connect to the WhyLabs platform to upload and store the generated whylogs data profiles, the Data Validator stack component does not require any configuration parameters. Adding it to a stack is as simple as running e.g.: