  During the last decade, Machine Learning (ML) has increasingly become a hot
topic in the field of Computer Networks and is expected to be gradually adopted
for a plethora of control, monitoring and management tasks in real-world
deployments. This poses the need to count on new generations of students,
researchers and practitioners with a solid background in ML applied to
networks. During 2020, the International Telecommunication Union (ITU) has
organized the "ITU AI/ML in 5G challenge'', an open global competition that has
introduced to a broad audience some of the current main challenges in ML for
networks. This large-scale initiative has gathered 23 different challenges
proposed by network operators, equipment manufacturers and academia, and has
attracted a total of 1300+ participants from 60+ countries. This paper narrates
our experience organizing one of the proposed challenges: the "Graph Neural
Networking Challenge 2020''. We describe the problem presented to participants,
the tools and resources provided, some organization aspects and participation
statistics, an outline of the top-3 awarded solutions, and a summary with some
lessons learned during all this journey. As a result, this challenge leaves a
curated set of educational resources openly available to anyone interested in
the topic.
