H2O-3 is an open-source machine learning platform made to be scalable and fast. While providing interfaces faimiliar to data scientists (Python, R, Scala, Web UI and others), H2O-3 itself is implemented in Java. It contains many of the most popular machine learning algorithms, including Gradient Boosting Machines, XGBoost, Generalized Linear Models, Deep Learning and much more. It is a distributed, scalable platform users can start with by simply running it on their laptops with minimal requirements and then taking it to the cloud, running large H2O clusters and processing vast amounts of data. An introduction to H2O's features and mission will be done in order to demonstrate the challenges faced while implementing such system. A look under H2O's hood follows, revealing some of the internal machanisms used to make machine learning algorithms distributed and fast. And what challenges does that bring. Finally, H2O is not an isolated island floating in the waters of machine learning only. Lots of engineering effort goes into integration with other systems, such as databases, file systems and distributed computing platform. Also, resulting models must be productionized. There will be a guided tour through the engineering of such parts, focusing on challenges introduced by the distributed nature of the system.