The broad variety of data streams utilized to probe materials ranging from simple 2D images, to 3D or higher dimensional hyperspectral data sets, to time series probing material evolution or 3D tomographic reconstructions require the development of versatile and autonomous data analysis algorithms. Typically, advanced algorithms to reduce the dimensionality of hyperspectral data, segment or recognize patterns in images, and classify features in multidimensional data sets are employed as separate or sequential instances as shown in Fig. (middle) , . It has been shown that unsupervised machine learning is capable to automatically segment different crystalline regions in atomic resolution images and video sequences solely based on crystal structure symmetry without requiring prior knowledge on the underlying structure . Using a trained Bayesian deep neural network , it is even possible to classify crystal structures in atomically resolved images and identify defective regions or interfaces by considering the uncertainty in the prediction . In a future direction, one would envision that novel big-data and machine-learning algorithms will be integrated in hybrid algorithm architectures that perform automatic or even autonomous tasks.