A Data Quality-Driven View of MLOps

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.