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Machine learning tasks such as regression, clustering, and classification are typically performed outside of database systems using dedicated tools, necessitating the extraction, transfor-mation, and loading of data. We argue that database systems when extended to enable automatic differentiation, gradient descent, and tensor algebra are capable of solving machine learning tasks more efficiently by eliminating the need for costly data communication. We demonstrate our claim by implementingdoi:10.18420/btw2019-16 dblp:conf/btw/SchuleSHKG019 fatcat:w6c3u335znctpdhgsiunenxkre