Developing machine learning models can be seen as a process similar to the one established for traditional
\nsoftware development. A key difference between the two lies in the strong dependency between the quality
\nof a machine learning model and the quality of the data used to train or perform evaluations. In this
\nwork, we demonstrate how different aspects of data quality propagate through various stages of machine
\nlearning development. By performing a joint analysis of the impact of well-known data quality dimensions
\nand the downstream machine learning process, we show that different components of a typical MLOps
\npipeline can be efficiently designed, providing both a technical and theoretical perspective.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 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