In the data-centric AI paradigm, improving data set quality often delivers greater performance gains than tuning the model itself. Cleanlab is an open-source Python library designed to address this challenge by automatically identifying common data issues — such as mislabeling, outliers and duplicates — across text, image, tabular and audio data sets. Built on the principle of confident learning, Cleanlab leverages model-predicted probabilities to estimate label noise and quantify data quality.
This model-agnostic approach enables developers to diagnose and correct data set errors, then retrain models for improved robustness and accuracy. Our teams have used Cleanlab successfully in production, confirming its effectiveness in real-world settings. We recommend it as a valuable tool for promoting data standardization and improving data set quality in AI engineering projects.
