A Data Quality-Driven View of MLOps

TL;DR

This paper explores how data quality influences the entire MLOps pipeline, emphasizing the importance of data quality dimensions for effective machine learning development and deployment.

Contribution

It provides a joint analysis of data quality dimensions and their impact on MLOps, offering both technical and theoretical insights into pipeline design.

Findings

Data quality dimensions significantly affect ML model performance.

Effective MLOps pipelines require integrated data quality management.

Theoretical framework for data quality propagation in ML workflows.

Abstract

Developing machine learning models can be seen as a process similar to the one established for traditional software development. A key difference between the two lies in the strong dependency between the quality of a machine learning model and the quality of the data used to train or perform evaluations. In this work, we demonstrate how different aspects of data quality propagate through various stages of machine learning development. By performing a joint analysis of the impact of well-known data quality dimensions and the downstream machine learning process, we show that different components of a typical MLOps pipeline can be efficiently designed, providing both a technical and theoretical perspective.