In this talk, I summarize Generalised Variational Inference—a family of methods that is geared towards scalable and robust inference in Bayesian machine learning models. I cover its relationship to other variational and Bayes-like methods, its modularity and the way it can be used to address various challenges in machine learning, as well as its applications to Bayesian Neural Networks, (Deep) Gaussian Processes, and Bayesian On-line Changepoint Detection in the presence of outliers.