  Machine learning models often learn latent embedding representations that
capture the domain semantics of their training data. These embedding
representations are valuable for interpreting trained models, building new
models, and analyzing new datasets. However, interpreting and using embeddings
can be challenging due to their opaqueness, high dimensionality, and the large
size of modern datasets. To tackle these challenges, we present WizMap, an
interactive visualization tool to help researchers and practitioners easily
explore large embeddings. With a novel multi-resolution embedding summarization
method and a familiar map-like interaction design, WizMap enables users to
navigate and interpret embedding spaces with ease. Leveraging modern web
technologies such as WebGL and Web Workers, WizMap scales to millions of
embedding points directly in users' web browsers and computational notebooks
without the need for dedicated backend servers. WizMap is open-source and
available at the following public demo link: https://poloclub.github.io/wizmap.
