Automated prediction is one of the major drivers of modern machine learning, be it weather prediction, protein folding or the prediction of readout signals in a measurement device. Generative modelling techniques have become the gold standard for a wide range of applications, with the most popular models including variational auto encoders (VAEs), generative adversarial networks (GANs), normalising flows or diffusion models. The automated detection of anomalies builds on this precise modelling of high-dimensional data distributions through generative modelling. Applications range from the search for the largest objects in space to the tiniest particles in the collisions of the Large Hadron Collider at CERN. In this panel, we intend to focus on early career scientists, inviting them to talk about their challenges and the proposed solutions, as well as how the approaches could be generalised.