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.

通过使用隐藏变体的阴影管道自动检测潜在问题、尝试修改以改善性能并向用户提出和解释这些修改的建议，我们提出在机器学习管道开发周期中通过交互式建议来支持数据科学家，并应用基于增量视图维护的优化来确保低延迟计算和维护阴影管道的可行性和潜在好处。