  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.
