Deep learning approaches such as convolutional neural nets have consistently demonstrated to outperform previous methods on challenging tasks such as dense, semantic segmentation. However, different models perform differently, with behaviour largely influenced by architectural choices and training settings. This paper explores ensembles of multiple models and architectures (EMMA) for robust performance through aggregation of predictions from a wide range of methods, and thus reducing the in uence of the meta-parameters of individual models and the risk of overfitting the configuration to a particular database. EMMA can be seen as an unbiased, generic deep learning model which is shown to yield excellent performance on the BRATS 2017 challenge.