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cs.DB2024
Messy Code Makes Managing ML Pipelines Difficult? Just Let LLMs Rewrite the Code!
Sebastian Schelter, Stefan Grafberger
Machine learning (ML) applications that learn from data are increasingly used to automate impactful decisions. Unfortunately, these applications often fall short of adequately mana…
cs.DB2024★ 3 cited
Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines"
Stefan Grafberger, Paul Groth, Sebastian Schelter
Data scientists develop ML pipelines in an iterative manner: they repeatedly screen a pipeline for potential issues, debug it, and then revise and improve its code according to the…