This is the first in a series of posts where we take a deeper dive into the question of data drift detection. We explore not only why it is an important part of model monitoring, but we also discuss regimes and approaches to keep in mind. In the first part of the series, we discuss drift in the context of Tabular data and describe univariate and multivariate techniques for tackling these problems.

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