Tidy Drift Detection for Monitored Machine Learning Models // Detects concept drift and data drift in streams produced by deployed machine learning models, using a tidy interface that composes with the 'tidymodels' ecosystem. Detectors are specified, fitted on a baseline period, and advanced over new batches of observations, returning tibbles annotated with warning and drift flags. A catalogue of 22 sequential drift detectors is provided. Error-based methods include the Drift Detection Method (DDM) of Gama et al. (2004) doi:10.1007/978-3-540-28645-5_29, the Early Drift Detection Method (EDDM) of Baena-Garcia et al. (2006), the Hoeffding's inequality based Drift Detection Methods (HDDM) of Frias-Blanco et al. (2015) doi:10.1109/TKDE.2014.2345382, and the Exponentially Weighted Moving Average (EWMA) chart of Ross et al. (2012) doi:10.1016/j.patrec.2011.08.019. Distribution-based methods include Adaptive Windowing (ADWIN) of Bifet and Gavalda (2007) doi:10.1137/1.9781611972771.42, Kolmogorov-Smirnov Windowing (KSWIN) of Raab et al. (2020) doi:10.1016/j.neucom.2019.11.111, and the Page-Hinkley test of Page (1954) doi:10.1093/biomet/41.1-2.100.