Sum-product networks (SPNs) are a prominent class of tractable probabilistic model, facilitating efficient marginalization, conditioning, and other inference routines. However, despite these attractive properties, SPNs have received rather little attention in the (probabilistic) deep learning community, which rather focuses on intractable models such as generative adversarial networks, variational autoencoders, normalizing flows, and autoregressive density estimators. In this talk, I discuss several recent endeavors which demonstrate that i) SPNs can be effectively used as deep learning models, and ii) that hybrid learning approaches utilizing SPNs and other deep learning models are in fact sensible and beneficial.