Data Quality in Machine Learning Systems
Machine Learning (ML) methods are standard components in modern software systems and influence our decisions every day. Often however it takes years to translate successes in research into useful ML innovations for end users. One of the reasons for this gap are challenges related to data quality.
In collaboration with Amazon Research and Prof. Sebastian Schelter at the University of Amsterdam we develop methods for better automation of monitoring (e.g. Schelter et al, VLDB, 2018) of data quality, improvement of data quality (e.g. Biessmann et al, CIKM, 2019) and prediction of data quality problems in ML production systems (e.g. Schelter et al, SIGMOD, 2020)
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