Computing and machine learning for the SHiP experiment

The recently approved SHiP experiment aims to search for new physics at the intensity frontier, including feebly interacting particles and light dark matter, and perform precision measurements of tau neutrinos. To fulfil its full discovery potential, the SHiP software framework is crucial, and faces some unique challenges due to the broad range of models under study, and the extreme statistics necessary for the background studies. The SHiP environment also offers unique opportunities for machine learning for detector design and anomaly detection. This contribution gives an overview of the general software framework and of past, ongoing and planned simulation and machine learning studies.