Paper accepted to Data Management for End-to-End Machine Learning Workshop (DEEM) @ SIGMOD 2023

DiffML: End-to-end Differentiable ML Pipelines


Authors: Benjamin Hilprecht, Christian Hammacher, Eduardo dos Reis, Mohamed Abdelaal, and Carsten Binnig

We demonstrate initial ideas and a general principle of how typical data engineering steps can be formulated as differentiable programs and jointly learned with the ML model.

Moreover, we discuss a research roadmap and core challenges that have to be systematically tackled to enable fully differentiable ML pipelines.

DiffML Pipeline
DiffML Pipeline