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Machine learning models often learn latent embedding representations thatcapture the domain semantics of their training data. These embeddingrepresentations are valuable for interpreting trained models, building newmodels, and analyzing new datasets. However, interpreting and using embeddingscan be challenging due to their opaqueness, high dimensionality, and the largesize of modern datasets. To tackle these challenges, we present WizMap, aninteractive visualization tool to help researchers and practitioners easilyexplore large embeddings. With a novel multi-resolution embedding summarizationmethod and a familiar map-like interaction design, WizMap enables users tonavigate and interpret embedding spaces with ease. Leveraging modern webtechnologies such as WebGL and Web Workers, WizMap scales to millions ofembedding points directly in users' web browsers and computational notebookswithout the need for dedicated backend servers. WizMap is open-source andavailable at the following public demo link: https://poloclub.github.io/wizmap.

WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings