Deployed machine learning models can fail spectacularly in response to seemingly benign changes to the underlying process being modelled. In this talk, we give a practical overview to drift detection, the discipline focused on detecting such changes. We will start by building an understanding of the ways in which drift can occur, why it pays to detect it, and how it can be detected in a principled manner. A range of drift detection strategies will be introduced, and we will examine how they can be applied to realistic high-dimensional datasets. We will then discuss specific considerations regarding drift deployment in production environments, where data often arrives continuously. To finish, we will demonstrate how the theory can be put into practice using the open-source alibi-detect Python library.