Meetup News
london information retrieval meetup
london information retrieval meetup

We are so happy to announce the eleventh London Information Retrieval Meetup, a free evening meetup aimed to Information Retrieval passionates and professionals who are curious to explore and discuss the latest trends in the field.

This time the meetup will be a hybrid event, both in-presence and online!


Location: Nine Elms Point – Wandsworth Rd Nine Elms, London, SW8 2FS, United Kingdom

Date: 14th December 2021 | 6:00-8:00 PM (GMT)

*Green pass and facial covering are required if you plan to attend in person


Registration required for zoom linkREGISTER HERE

Date: 14th December 2021 | 6:15-8:00 PM (GMT)

The event will be structured with 2 technical talks, with a Q&A session after each talk.



[starting at 6:15]
After a short welcome & latest news speech from our Founder Alessandro Benedetti, we will proceed to the first talk.

First talk


Jo works as a distinguished engineer at Yahoo where he spends his time working on Vespa, the open-source big data serving engine. 


Taking the neural search paradigm shift to production

Search is going through a paradigm shift, sometimes referred to as the “BERT revolution.” The introduction of pre-trained language transformer models like BERT has brought significant advancements in search and document ranking state-of-the-art.

Bringing these promising methods to production in an end-to-end search serving system is not trivial. It requires substantial middleware glue and deployment effort to connect open-source tools like Apache Lucene, vector search libraries (e.g., FAISS), and model inference servers. However, the open-source serving engine Vespa, which Yahoo has developed since 2003, offers features that enable implementing state-of-the-art retrieval and ranking methods using a single serving engine stack, significantly reducing deployment complexity, cost, and failure modes.

This talk gives an overview of the Vespa search serving architecture and features enabling expressing state-of-the-art retrieval and ranking methods. We dive into Vespa’s implementations of sub-linear retrieval algorithms for sparse and dense representations to produce candidate documents for (re-)ranking efficiently. Vespa allows expressing the end-to-end multi-stage retrieval and ranking pipeline, including inference using transformer models. We also touch on real-world application constraints, such as filtering and search result diversification. 

…second talk is coming soon


Lisa Biella

Lisa Biella is a creative digital marketer, geek at heart who is enthusiastic about technology and how it affects people’s lives.

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