Besides providing the framework for performing custom-made AI analysis, the NOMAD AI toolkit provides a set of tutorial notebooks introducing users step by step into both the most popular and widely known AI methodologies, with showcase applications in materials science, and into the more advanced ones, i.e., methodologies that have been published in the latest years. Due to the very nature of the Jupyter technology, these tutorial notebooks are interactive, in the sense that users can modify lines of codes and check the effect of the modifications. Also, the tutorial notebooks have direct access to the whole NOMAD data, so that users can apply the learned techniques to new data, including data uploaded by them. Importantly, the AI toolkit includes notebooks that present actual AI software as used for producing results for peer-reviewed publications. This feature suggests that scientific reproducibility can reach its full potential, at least for AI analysis tools. For instance, users can re-train AI models with exactly the same set of hyperparameters as used in the original publications, on exactly the same data, including the train/validation/test set splits. A piece of information that nowadays is not required in peer-reviewed publications. However, such addition would be scientifically appropriate as it would directly enable the reproducibility of reported results. The NOMAD AI toolkit enables this important step. As already noted by the proponents of the FAIR principles for scientific software , providing complete information on the algorithms and software used to analyze data is all but trivial. This is particularly challenging if one wants to provide live software that can be run on demand, mainly because pieces of software, e.g Python scripts for an AI analysis, require a virtual environment where libraries that are used for efficiently performing certain routine tasks are installed. These libraries get repeatedly updated, and unfortunately backward compatibility is not necessarily ensured. This means that the same set of commands that at release time allows to install and run a software, at a later point in time may not yield a correct installation. Besides, in the case that the software is run in a container (as for the NOMAD AI toolkit), when a new container is created the software for the container platform gets updated. In other words, special care and planning has to be devoted to maintaining the whole ecosystem of software so that exactly the same datasets yield in time exactly the same AI models and therefore exactly the same predictions over the same test data.