Despite initial fears of a renewed AI winter, the current summer seems to have no end in sight. AI in general and Deep Learning in particular are permeating a growing number of our daily applications with an undeniable added value. As with Computer Science itself, this increasing entanglement with our lives calls for a special attention to safety and demands adequate verification and validation methods and tools to assist in the development of reliable AI. In this paper, we present the AIMOS tool as well as the results of its application to industrial use cases. Relying on the widely used Metamorphic testing paradigm, we show how the process of verification and validation can benefit from the early testing of models’ robustness to perturbations stemming from the intended operational domain.