🧪Data Validators

How to enhance and maintain the quality of your data and the performance of your models with data profiling and validation

Without good data, even the best machine learning models will yield questionable results. A lot of effort goes into ensuring and maintaining data quality not only in the initial stages of model development, but throughout the entire machine learning project lifecycle. Data Validators are a category of ML libraries, tools and frameworks that grant a wide range of features and best practices that should be employed in the ML pipelines to keep data quality in check and to monitor model performance to keep it from degrading over time.

Data profiling, data integrity testing, data and model drift detection are all ways of employing data validation techniques at different points in your ML pipelines where data is concerned: data ingestion, model training and evaluation and online or batch inference. Data profiles and model performance evaluation results can be visualized and analyzed to detect problems and take preventive or correcting actions.

Related concepts:

the Data Validator is an optional type of Stack Component that needs to be registered as part of your ZenML Stack.

Data Validators used in ZenML pipelines usually generate data profiles and data quality check reports that are versioned and stored in the Artifact Store and can be retrieved and visualized later.

When to use it

Data-centric AI practices are quickly becoming mainstream and using Data Validators are an easy way to incorporate them into your workflow. These are some common cases where you may consider employing the use of Data Validators in your pipelines:

early on, even if it's just to keep a log of the quality state of your data and the performance of your models at different stages of development.