Mismatch between training and deployment data, known as distributional shift, adversely impacts ML models and is ubiquitous in real, industrial applications. In this competition the contestants’ goal is to develop models which are both robust to distributional shift and can detect it via uncertainty estimation. The broad aim of this competition is to raise awareness of the issue and stimulate the community to work on tasks and modalities taken from large-scale industrial applications. Thus, we provide the "Shifts Dataset" - a new, large dataset of genuine `in the wild' examples of distributional shift from weather prediction, machine translation, and vehicle motion prediction. Each task represents a particular data-modality and is uniquely challenging. Each task will have an associated competition track with prizes for top contestants.