Conformal prediction, once a small research niche, is now drawing attention from the machine learning main stream, e.g. Amazon’s implementation within the new Fortuna library. Conformal prediction adds uncertainty quantification to any machine learning model, the confidence regions it produces are provably reliable, and it does not need unrealistically strong assumptions. Applications to classification, time series, and causal machine learning will be shown in a hands-on manner.