Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines"-Reference-Cited by-同舟云学术

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

Published:2024-06-09 Issue: Volume: Page:7-11
ISSN:
Container-title:Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning
language:
Short-container-title:

Author:

Grafberger Stefan¹^ORCID,Groth Paul²^ORCID,Schelter Sebastian³^ORCID

Affiliation:

1. AIRLab, University of Amsterdam, The Netherlands

2. University of Amsterdam, The Netherlands

3. BIFOLD & TU Berlin, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3650203.3663327

Reference27 articles.

1. Sarah Bird, Miro Dudík, Richard Edgar, Brandon Horn, Roman Lutz, Vanessa Milan, Mehrnoosh Sameki, Hanna Wallach, and Kathleen Walker. 2020. Fairlearn: A toolkit for assessing and improving fairness in AI. Microsoft, Tech. Rep. MSR-TR-2020-32 (2020).

2. Eric Breck, Neoklis Polyzotis, Sudip Roy, Steven Whang, and Martin Zinkevich. 2019. Data Validation for Machine Learning. MLSys (2019).

3. Slice Finder: Automated Data Slicing for Model Validation

4. GitHub. 2021. GitHub Copilot Your AI pair programmer. https://copilot.github.com/.

5. Stefan Grafberger, Paul Groth, and Sebastian Schelter. 2023. Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines. SIGMOD (2023).

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