Grant Ingersoll - Fractional CTO, Leading Search Consultant - Engineering Better Search

Vector Podcast Live

Topics:

00:00 Kick-off introducing co:rise study platform

03:03 Grant’s background

04:58 Principle of 3 C’s in the life of a CTO: Code, Conferences and Customers

07:16 Principle of 3 C’s in the Search Engine development: Content, Collaboration and Context

11:51 Balance between manual tuning in pursuit to learn and Machine Learning

15:42 How to nurture intuition in building search engine algorithms

18:51 How to change the approach of organizations to true experimentation

23:17 Where should one start in approaching the data (like click logs) for developing a search engine

29:36 How to measure the success of your search engine

33:50 The role of manual query rating to improve search result relevancy

36:56 What are the available datasets, tools and algorithms, that allow us to build a search engine?

41:56 Vector search and its role in broad search engine development and how the profession is shaping up

49:01 The magical question of WHY: what motivates Grant to stay in the space

52:09 Announcement from Grant: course discount code DGSEARCH10

54:55 Questions from the audience

Show notes:

- Grant’s interview at Berlin Buzzwords 2016: https://www.youtube.com/watch?v=Y13gZM5EGdc

- “BM25 is so Yesterday: Modern Techniques for Better Search”: https://www.youtube.com/watch?v=CRZfc9lj7Po

- “Taming text” - book co-authored by Grant: https://www.manning.com/books/taming-text

- Search Fundamentals course - https://corise.com/course/search-fundamentals

- Search with ML course - https://corise.com/course/search-with-machine-learning

- Click Models for Web Search: https://github.com/markovi/PyClick

- Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing, book by Ron Kohavi et al: https://www.amazon.com/Trustworthy-Online-Controlled-Experiments-Practical-ebook/dp/B0845Y3DJV

- Quepid, open source tool and free service for query rating and relevancy tuning: https://quepid.com/

- Grant’s talk in 2013 where he discussed the need of a vector field in Lucene and Solr: https://www.youtube.com/watch?v=dCCqauwMWFE

- CLIP model for multimodal search: https://openai.com/blog/clip/

- Demo of multimodal search with CLIP: https://blog.muves.io/multilingual-and-multimodal-vector-search-with-hardware-acceleration-2091a825de78

- Learning to Boost: https://www.youtube.com/watch?v=af1dyamySCs

- Dmitry’s Medium List on Vector Search: https://medium.com/@dmitry-kan/list/vector-search-e9b564d14274