  Hadronization is a complex quantum process whereby quarks and gluons become
hadrons. The widely-used models of hadronization in event generators are based
on physically-inspired phenomenological models with many free parameters. We
propose an alternative approach whereby neural networks are used instead. Deep
generative models are highly flexible, differentiable, and compatible with
Graphical Processing Unit (GPUs). We make the first step towards a data-driven
machine learning-based hadronization model by replacing a compont of the
hadronization model within the Herwig event generator (cluster model) with a
Generative Adversarial Network (GAN). We show that a GAN is capable of
reproducing the kinematic properties of cluster decays. Furthermore, we
integrate this model into Herwig to generate entire events that can be compared
with the output of the public Herwig simulator as well as with $e^+e^-$ data.
