# Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines"

arXiv: https://arxiv.org/abs/2404.19591 · HTML: https://mlsanity.com/paper/2404.19591/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 30 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms](https://mlsanity.com/paper/1908.08619/) (2019)
- [Interpretable Data-Based Explanations for Fairness Debugging](https://mlsanity.com/paper/2112.09745/) (2021)
- [Complaint-driven Training Data Debugging for Query 2.0](https://mlsanity.com/paper/2004.05722/) (2020)
- [CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks](https://mlsanity.com/paper/1904.09483/) (2019)

## Built on by (most cited first, 1 of 1)

- [Instrumentation and Analysis of Native ML Pipelines via Logical Query Plans](https://mlsanity.com/paper/2407.07560/) (2024)

## Abstract (arXiv)

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.