Embedding Atlas is a tool that provides interactive visualizations for large embeddings. It allows you to visualize, cross-filter, and search embeddings and metadata. Open sourced by Apple.
Compute & interactively visualize large embeddings
Embedding Atlas is a tool that provides interactive visualizations for large embeddings. It allows you to visualize, cross-filter, and search embeddings and metadata. Open sourced by Apple.
Looks like Apple is letting their research publish open source projects! If so — that's great news for the OSS ecosystem. Embedding Atlas Features: 🏷️ Automatic data clustering & labeling: Interactively visualize and navigate overall data structure. 🫧 Kernel density estimation & density contours: Easily explore and distinguish between dense regions of data and outliers. 🧊 Order-independent transparency: Ensure clear, accurate rendering of overlapping points. 🔍 Real-time search & near
Very cool interactive visualization for large embeddings is hugely valuable for debugging and exploration. Curious about performance on million‑vector scales and support for custom distance metrics. We’re debuting today as well keen to hear what you think.
I work with large embedding datasets—this visualization saves hours. Can it handle real-time updates or streaming data sources?
Almost exactly what I need now for my PhD!!!
Very cool UI for data scientist. Since I used nomic atlas, I was fascinated by this efficient way to explore large-scale dataset. Fantastic work by Apple!
Categories come from the product's launch tags. Most products appear in 2-3 categories. The primary category is listed first.
The scores reflect launch-period engagement. Historical data is preserved and doesn't change retroactively. The build date at the bottom shows when the index was last refreshed.
Check the similar products section on this page, or browse the category pages linked in the tags above. Each category page shows all products for a given year, sorted by engagement.
A measure of community engagement at launch. Higher means more people noticed and interacted with the product. It's a traction signal, not a quality rating.