Pushing ML Predictions into DBMSs (Extended Abstract)

We explore the use of Relational Database Manage-ment Systems to reduce technical debt in Machine Learning de-ployments, specifically focusing on in-DBMS prediction serving. We evaluate the performance of ML pipelines in Sklearn and ML.NET against their SQL counterparts executed on MySQL and SQL Server. Results suggest comparable performance when data resides in the database, indicating the potential feasibility of executing prediction processes directly in SQL on DBMSs.