Episode description
Topics:
00:00 Introduction
01:21 Jo Kristian’s background in Search / Recommendations since 2001 in Fast Search & Transfer (FAST)
03:16 Nice words about Trondheim
04:37 Role of NTNU in supplying search talent and having roots in FAST
05:33 History of Vespa from keyword search
09:00 Architecture of Vespa and programming language choice: C++ (content layer), Java (HTTP requests and search plugins) and Python (pyvespa)
13:45 How Python API enables evaluation of the latest ML models with Vespa and ONNX support
17:04 Tensor data structure in Vespa and its use cases
22:23 Multi-stage ranking pipeline use cases with Vespa
24:37 Optimizing your ranker for top 1. Bonus: cool search course mentioned!
30:18 Fascination of Query Understanding, ways to implement and its role in search UX
33:34 You need to have investment to get great results in search
35:30 Game-changing vector search in Vespa and impact of MS Marco Passage Ranking
38:44 User aspect of vector search algorithms
43:19 Approximate vs exact nearest neighbor search tradeoffs
47:58 Misconceptions in neural search
52:06 Ranking competitions, idea generation and BERT bi-encoder dream
56:19 Helping wider community through improving search over CORD-19 dataset
58:13 Multimodal search is where vector search shines
1:01:14 Power of building fully-fledged demos
1:04:47 How to combine vector search with sparse search: Reciprocal Rank Fusion
1:10:37 The philosophical WHY question: Jo Kristian’s drive in the search field
1:21:43 Announcement on the coming features from Vespa
- Jo Kristian’s Twitter: https://twitter.com/jobergum
- Dmitry’s Twitter: https://twitter.com/DmitryKan
For the Show Notes check: https://www.youtube.com/watch?v=UxEdoXtA9oM