2024-06-29, 10:55–11:20 (US/Pacific), Barn
Vector embeddings are a way to encode a text or image as an array of floating point numbers, and they make it possible to perform similarity search on many kinds of content. Let's try to wrap our head around vector embeddings and similarity spaces by exploring them visually! We'll compare different embedding models, different quantization schemes, and different input modalities, using open source tools that produce graphs and charts. Come on a vector voyage!
Pamela is currently a Principal Cloud Advocate in Python at Microsoft. Previously, she was a lecturer for UC Berkeley, the creator of the computer programming curriculum for Khan Academy, an early engineer at Coursera, and a developer advocate at Google.