Often times we work with data that is considered noisy. But what happens when we want to propagate noise through our machine learning model? Gaussian processes are notorious for having a kernel Bayesian framework that allows us to get predictive mean and variances. I explore a few methods that would allow us to handle the uncertainties including the easy linearized version (the Taylor expansion) and a variational method. While this talk is very short and doesn’t include too many examples, I hope to outline the most important aspects so that people can start getting involved.