  Machine Learning (ML) is increasingly used to automate impactful decisions,
which leads to concerns regarding their correctness, reliability, and fairness.
We envision highly-automated software platforms to assist data scientists with
developing, validating, monitoring, and analysing their ML pipelines. In
contrast to existing work, our key idea is to extract "logical query plans"
from ML pipeline code relying on popular libraries. Based on these plans, we
automatically infer pipeline semantics and instrument and rewrite the ML
pipelines to enable diverse use cases without requiring data scientists to
manually annotate or rewrite their code.
  First, we developed such an abstract ML pipeline representation together with
machinery to extract it from Python code. Next, we used this representation to
efficiently instrument static ML pipelines and apply provenance tracking, which
enables lightweight screening for common data preparation issues. Finally, we
built machinery to automatically rewrite ML pipelines to perform more advanced
what-if analyses and proposed using multi-query optimisation for the resulting
workloads. In future work, we aim to interactively assist data scientists as
they work on their ML pipelines.
