  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.
