Obviously, this predictive methodology of deep learning has potential in many application areas, conceivably including materials science and particularly microscopy images. However, the success of deep learning builds on various assumptions, including the availability of large training data with 'independent and identically distributed' (iid) samples. These assumptions are not easily satisfied for materials data, and feature engineering and physics-based modeling is still indispensable. [e.g. At its core, ML operates as an interpolation technique, fitting and connecting the data upon which it is trained, applying regularization (or smoothening) to achieve generalization. The ML model excels in exploiting the data space covered by the training data but exhibits diminished reliability when entering uncharted data realms typically called the out-of-distribution (OOD) regime. When the training data are iid or representative of the full population, extrapolation may work. However, for materials science this requirement is hardly fulfilled, i.e., the data selection is governed by subjective and technical issues, and often it is strongly biased and unbalanced. Still, materials scientists are searching for statistically exceptional situations, and important processes are often triggered by 'rare events' that are not or not well covered by the available data set, or smoothed out by the regularization. [e.g. Ref This all implies caution when applying ML.