Tom Hengl is the co-founder of OpenGeoHub Foundation, the Netherlands, and leader of the Work Package “Dissemination, project sustainability, and impact assessment” of the MOOD project. As a PhD candidate and research within OpenGeoHub Foundation, Carmelo focuses on data science projects such as GeoHarmonizer and the MOOD H2020 project. In this lecture of the 2023 MOOD Summer School, Tom and Carmelo showed how to do space-time machine learning using ensemble of machine learning methods. He used an example of the SAVSNET (Small Animal Veterinary Surveillance Network) dataset containing over seven million spatial point records, among which 0.16% with tick attachment. He and his team overlayed these points with over seventy covariates to produce space-time monthly and long term predictions for the period between 2014 and 2021.