  The reconstruction of neutrino events in the IceCube experiment is crucial
for many scientific analyses, including searches for cosmic neutrino sources.
The Kaggle competition "IceCube -- Neutrinos in Deep ice" was a public machine
learning challenge designed to encourage the development of innovative
solutions to improve the accuracy and efficiency of neutrino event
reconstruction. Participants worked with a dataset of simulated neutrino events
and were tasked with creating a suitable model to predict the direction vector
of incoming neutrinos. From January to April 2023, hundreds of teams competed
for a total of $50k prize money, which was awarded to the best performing few
out of the many thousand submissions. In this contribution I will present some
insights into the organization of this large outreach project, and summarize
some of the main findings, results and takeaways.
