  Modern machine learning techniques commonly rely on complex, high-dimensional
embedding representations to capture underlying structure in the data and
improve performance. In order to characterize model flaws and choose a
desirable representation, model builders often need to compare across multiple
embedding spaces, a challenging analytical task supported by few existing
tools. We first interviewed nine embedding experts in a variety of fields to
characterize the diverse challenges they face and techniques they use when
analyzing embedding spaces. Informed by these perspectives, we developed a
novel system called Emblaze that integrates embedding space comparison within a
computational notebook environment. Emblaze uses an animated, interactive
scatter plot with a novel Star Trail augmentation to enable visual comparison.
It also employs novel neighborhood analysis and clustering procedures to
dynamically suggest groups of points with interesting changes between spaces.
Through a series of case studies with ML experts, we demonstrate how
interactive comparison with Emblaze can help gain new insights into embedding
space structure.
