Session Outline
The quality of data-powered applications depends not only on code, but also on collected data, as well as models trained on data. This renders traditional quality assurance inadequate. We will take a look in our toolbox for more holistic tactics that bridge the gap between code and data quality assurance.
Key Takeaways
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