We propose to replace the exact amplitudes used in MC event generators for trained Machine Learning regressors, with the aim of speeding up the evaluation of {it slow} amplitudes. As a proof of concept, we study the process $gg to ZZ$ whose LO amplitude is loop induced. We show that gradient boosting machines like $texttt{XGBoost}$ can predict the fully differential distributions with errors below $0.1 %$, and with prediction times $mathcal{O}(10^3)$ faster than the evaluation of the exact function. This is achieved with training times $sim 7$ minutes and regressors of size $lesssim 30$~Mb. These results suggest a possible new avenue to speed up MC event generators.
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