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

TL;DR

sql4ml enables expressing and training machine learning models entirely within SQL, streamlining the workflow by integrating data preprocessing, feature engineering, and model training in a single system.

Contribution

The paper introduces sql4ml, a system that translates SQL-based ML workflows into TensorFlow, simplifying the data science process by unifying it within a database environment.

Findings

Successfully applied to three well-known ML algorithms

Demonstrated usability benefits of integrated workflow

Achieved end-to-end ML training within SQL environment

Abstract

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