This paper briefly summarizes the ALASKA#2 steganalysis challenge which has been organized on the Kaggle machine learning competition platform. We especially focus on the context, the organization (rules, timeline, evaluation and material) as well as on the outcome (number of competitors, submission, findings, and final results). While both steganography and steganalysis were new to most of the competitors, they were able to leverage their skills in Deep Learning in order to design detection methods that perform significantly better than current art in steganalysis. Despite the fact that these solutions come at an important computational cost, they clearly indicate new directions to explore in steganalysis research.