Here I mention some classical approach for data science projects and show why it is not enough. The metadata can help then to control the differences between serving and training data. Moreover metadata can be used for creation of model unit tests.

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4. Preparin g data can be a big effort
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10. Al) Quality in ert → Action Action
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12. /Metadata for Data Model) Quality in Qual) Quality in ity in
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13. Havin g the right data is: crucial) Quality in

14. Referen ces:
● , ,Dan il) Quality in o Sato Arif Wider Chris: toph Win dheus: er
“Con tin uous: Del) Quality in ivery in for Machin e L - earn in g”
● , . , ,Al) Quality in kis: Pol) Quality in y in zotis: Martin A Zin kevich Steven Whan g Sudip Roy in “Data
, 2017Man agemen t Chal) Quality in l) Quality in en ges: in Production Machin e L - earn in g “ ICMD
● , , , , ,Eric B7507001reck Neokl) Quality in is: Pol) Quality in y in zotis: Sudip Roy in Steven Euijon g Whan g Martin
, 19Zin kevich “Data val) Quality in idation for machin e l) Quality in earn in g” Sy in s: ML - ’
● :// . . / / - - - -https: www s: cien tificamerican com articl) Quality in e how a machin e l) Quality in earn s:
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