A data centric AI framework for automating exploratory data analysis and data quality tasks

Democratisation of machine learning (ML) has been an important theme in the research community for the last several years with notable progress made by the model-building community with automated machine learning models. However, data plays a central role in building ML models and there is a need to focus on data-centric AI innovations. In this paper, we first map the steps taken by data scientists for the data preparation phase and identify open areas and pain points via user interviews. We then propose a framework and four novel algorithms for exploratory data analysis and data quality for AI steps addressing the pain points from user interviews. We also validate our algorithms with open-source datasets and show the effectiveness of our proposed methods. Next, we build a tool that automatically generates python code encompassing the above algorithms and study the usefulness of these algorithms via two user studies with data scientists. We observe from the first study results that the participants who used the tool were able to gain 2X productivity and 6% model improvement over the control group. The second study is performed in a more realistic environment to understand how the tool would be used in real-world scenarios. The results from this study are coherent with the first study and show an average of 30-50% of time savings that can be attributed to the tool.