In order to combine Deep Learning and proper uncertainty estimation, we developed Borch.
A scalable deep universal probabilistic programming language, built on top of PyTorch
makes it easy to write anything from hierarchical Bayesian models to probabilistic deep neural networks with billions of parameters. We even have functionalities for taking your favorite neural network architecture and converting it to Bayesian in one line of code! Why does this matter? Well, because all models in production need to be able to show their uncertainty on any given prediction. This makes deep learning more transparent, explainable, and robust.