A Data Quality-Driven View of MLOps
This work addresses data quality issues in MLOps for practitioners, but it is incremental as it builds on known data quality dimensions without introducing new methods.
The paper tackles the problem of data quality's impact on machine learning development by analyzing how data quality dimensions propagate through MLOps stages, showing that pipeline components can be efficiently designed from both technical and theoretical perspectives.
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.