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

Topics:00:00 Introduction01:21 Jo Kristian’s background in Search / Recommendations since 2001 in Fast Search & Transfer (FAST)03:16 Nice words about Trondheim04:37 Role of NTNU in supplying search talent and having roots in FAST 05:33 History of Vespa from keyword search09:00 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 support17:04 Tensor data structure in Vespa and its use cases22:23 Multi-stage ranking pipeline use cases with Vespa24:37 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 UX33:34 You need to have investment to get great results in search35:30 Game-changing vector search in Vespa and impact of MS Marco Passage Ranking38:44 User aspect of vector search algorithms43:19 Approximate vs exact nearest neighbor search tradeoffs47:58 Misconceptions in neural search52:06 Ranking competitions, idea generation and BERT bi-encoder dream56:19 Helping wider community through improving search over CORD-19 dataset58:13 Multimodal search is where vector search shines1:01:14 Power of building fully-fledged demos1:04:47 How to combine vector search with sparse search: Reciprocal Rank Fusion1:10:37 The philosophical WHY question: Jo Kristian’s drive in the search field1:21:43 Announcement on the coming features from Vespa- Jo Kristian’s Twitter: https://twitter.com/jobergum- Dmitry’s Twitter: https://twitter.com/DmitryKanFor the Show Notes check: https://www.youtube.com/watch?v=UxEdoXtA9oM