Jo Bergum — Distinguished Engineer, Yahoo! Vespa — Journey of Vespa from Sparse into Neural Search

We just recorded this episode of Vector Podcast with Jo Kristian Bergum.

Photo by Hello I'm Nik on Unsplash

I learn a ton by following Jo Kristian’s stream of papers / code / lessons / ideas and thoroughly enjoyed recording this episode with him — grab a cup of your favorite beverage, listen, learn and enjoy!

Vector Podcast Episode with Jo Kristian Bergum on YouTube

Spotify: https://open.spotify.com/episode/5eiywuzKrRRcd1EaUp4ZMo

Apple Podcasts: https://podcasts.apple.com/fi/podcast/jo-bergum-distinguished-engineer-yahoo-vespa-journey/id1587568733?i=1000557237074

A few high-level topics from the episode:

- History of Vespa
- Tensor data structure and its use cases
- Multi-stage ranking pipeline
- Game-changing vector search in Vespa
- Approximate vs exact nearest neighbor search tradeoffs
- Misconceptions in neural search
- Multimodal search is where vector search shines
- Power of building fully-fledged demos
- How to combine vector search with sparse search: Reciprocal Rank Fusion
- The question of WHY (my favourite ❤️)

It also turned out, that a few podcasts and public talks I did were relevant to this episode, so you can find them as timed cards in the YouTube version. Hope this helps to learn even more on the related topics of vector search techniques and core ANN algorithms.

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Dmitry Kan

Founder and host of Vector Podcast, tech team lead, software engineer, manager, but also: cat lover and cyclist. Host: https://www.youtube.com/c/VectorPodcast