Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines"
<|reference_start|>Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines": 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 their findings. However, this manual process is tedious and error-prone. Therefore, we propose to support data scientists during this development cycle with automatically derived interactive suggestions for pipeline improvements. We discuss our vision to generate these suggestions with so-called shadow pipelines, hidden variants of the original pipeline that modify it to auto-detect potential issues, try out modifications for improvements, and suggest and explain these modifications to the user. We envision to apply incremental view maintenance-based optimisations to ensure low-latency computation and maintenance of the shadow pipelines. We conduct preliminary experiments to showcase the feasibility of our envisioned approach and the potential benefits of our proposed optimisations.<|reference_end|>
@article{grafberger2024towards,
    title={Towards Interactively Improving ML Data Preparation Code via "Shadow
  Pipelines"},
    author={Stefan Grafberger, Paul Groth, Sebastian Schelter},
    journal={arXiv preprint arXiv:2404.19591},
    year={2024},
    doi={10.1145/3650203.3663327},
    archivePrefix={arXiv},
    eprint={2404.19591},
    primaryClass={cs.DB cs.LG cs.SE}
}