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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.
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.
- if you have pipelines that regularly ingest new data, you should use data validation to run regular data integrity checks to signal problems before they are propagated downstream.
- in continuous training pipelines, you should use data validation techniques to compare new training data against a data reference and to compare the performance of newly trained models against previous ones.
- when you have pipelines that automate batch inference or if you regularly collect data used as input in online inference, you should use data validation to run data drift analyses and detect training-serving skew, data drift and model drift.
Data Validator are optional stack components provided by integrations. The following table lists the currently available Data Validators and summarizes their features and the data types and model types that they can be used with in ZenML pipelines:
If you would like to see the available flavors of Data Validator, you can use the command:
zenml data-validator flavor list
Every Data Validator has different data profiling and testing capabilities and uses a slightly different way of analyzing your data and your models, but it generally works as follows:
- first, you have to configure and add an Data Validator to your ZenML stack
- every integration includes one or more builtin data validation steps that you can add to your pipelines. Of course, you can also use the libraries directly in your own custom pipeline steps and simply return the results (e.g. data profiles, test reports) as artifacts that are versioned and stored by ZenML in its Artifact Store.
Last modified 1mo ago