Inside the Architecture Powering Data Quality Management at Uber
Data Quality Monitor implements novel statistical methods for anomaly detection and quality management in large data infrastructures.
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Data quality management is one of those often forgotten aspects of machine learning workflows. Small inconsistencies or missing values can have a drastic negative impact on the training of machine learning models. In any medium to large organization, the proliferation of disparate data sources make their quality control a tremendous challenge. In the case of Uber, the transportation giant relies on thousands of data sources to power machine learning processes that ensure missing critical…
