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