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We present sql4ml, a system for expressing supervised machine learning (ML)models in SQL and automatically training them in TensorFlow. The primarymotivation for this work stems from the observation that in many data sciencetasks there is a back-and-forth between a relational database that stores thedata and a machine learning framework. Data preprocessing and featureengineering typically happen in a database, whereas learning is usuallyexecuted in separate ML libraries. This fragmented workflow requires from theusers to juggle between different programming paradigms and software systems.With sql4ml the user can express both feature engineering and ML algorithms inSQL, while the system translates this code to an appropriate representation fortraining inside a machine learning framework. We describe our translationmethod, present experimental results from applying it on three well-known MLalgorithms and discuss the usability benefits from concentrating the entireworkflow on the database side.

sql4ml A declarative end-to-end workflow for machine learning