  Distributional shift, or the mismatch between training and deployment data,
is a significant obstacle to the usage of machine learning in high-stakes
industrial applications, such as autonomous driving and medicine. This creates
a need to be able to assess how robustly ML models generalize as well as the
quality of their uncertainty estimates. Standard ML baseline datasets do not
allow these properties to be assessed, as the training, validation and test
data are often identically distributed. Recently, a range of dedicated
benchmarks have appeared, featuring both distributionally matched and shifted
data. Among these benchmarks, the Shifts dataset stands out in terms of the
diversity of tasks as well as the data modalities it features. While most of
the benchmarks are heavily dominated by 2D image classification tasks, Shifts
contains tabular weather forecasting, machine translation, and vehicle motion
prediction tasks. This enables the robustness properties of models to be
assessed on a diverse set of industrial-scale tasks and either universal or
directly applicable task-specific conclusions to be reached. In this paper, we
extend the Shifts Dataset with two datasets sourced from industrial, high-risk
applications of high societal importance. Specifically, we consider the tasks
of segmentation of white matter Multiple Sclerosis lesions in 3D magnetic
resonance brain images and the estimation of power consumption in marine cargo
vessels. Both tasks feature ubiquitous distributional shifts and a strict
safety requirement due to the high cost of errors. These new datasets will
allow researchers to further explore robust generalization and uncertainty
estimation in new situations. In this work, we provide a description of the
dataset and baseline results for both tasks.
