ORCID
- Keith Quille: 0000-0002-1414-5142
Abstract
Preprocessing tools for data are increasingly being utilized in MLOps pipelines to develop models automatically. However, the fairness and reliability of automated processes are inadequately researched, risking causing performance degradation or bias. This discrepancy is addressed in this thesis with an evaluation of automated data preprocessing methods compared to a baseline approach, designed for integration into a TensorFlow Extended (TFX) pipeline. The performance of each method was compared in terms of classification measures and subgroup fairness to determine potential bias. Significance tests were employed to compare the performance of each automated method against the baseline. The results indicate that around half the automated methods had performance comparable to the baseline model, while the others performed much worse; more crucially, none of the automated methods significantly outperformed the baseline. These results show that not all preprocessing methods in automation can be used without manual validation.
Keywords
Data Preprocessing, Fairness in Machine Learning, MLOps Pipelines, Outlier Detection and Imputation, TensorFlow Data Validation (TFDV), TensorFlow Extended (TFX)
DOI Link
Publication Date
2026-02-16
Event
3rd International Conference on Human-Centred AI - Education and Practice, HCAI-ep 2026
Publication Title
HCAI-ep 2026 - Proceedings of the 2026 Conference on Human Centered Artificial Intelligence - Education and Practice
Publisher
Association for Computing Machinery (ACM)
ISBN
9798400721533
First Page
40
Last Page
45
Deposit Date
2026-05-14
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional Links
Recommended Citation
Ayinavilli, Surya Teja Gowd; Quille, Keith; and Singh, Tarry, "Data Preprocessing Methods for Automating MLOps Pipelines: A Comparative Study" (2026). Research Outputs: 2025-Present. 5.
https://arrow.tudublin.ie/faccomentro/5