  In pursuit of precise and fast theory predictions for the LHC, we present an
implementation of the MadNIS method in the MadGraph event generator. A series
of improvements in MadNIS further enhance its efficiency and speed. We validate
this implementation for realistic partonic processes and find significant gains
from using modern machine learning in event generators.
