Demonstration of OpenDBML, a framework for democratizing in-database machine learning

Machine learning over relational data has been used in several applications. The traditional approach of joining relations first and then training a model on the joined table is time-consuming and requires a significant amount of memory. Recent research has focused on in-database machine learning (in-DB ML) to address this issue; these methods train the models over relations without joining, resulting in a more efficient process. However, such systems have ad-hoc user interfaces and specific data formats, making them challenging to use. To address this problem, this paper presents OpenDBML, a framework for democratizing in-DB ML. OpenDBML offers a Python interface for multiple in-DB ML systems, a set of commonly used datasets, and the ability to add new datasets and in-DB ML systems via both Python and web interfaces. The paper also presents comprehensive demonstration scenarios to illustrate how to use OpenDBML effectively.