How We Chose our Vector Database: A Case Study - ft. Suhas Pai
At my company Hudson Labs, we have adopted a vector database to streamline our operations. As part of this process, the first few questions included 'do we really need a vector db'? 'which vector db to choose among the plethora available'? In this talk. I will walk you through the selection criteria we adopted for choosing the vector database that best suited our requirements, and showcase how to resolve various tradeoffs. I will also talk about the internals of the popular vector database frameworks and how they can affect downstream user choice.
Suhas Pai (CTO @ Hudson Labs)
Suhas is the CTO & Co-founder of Hudson Labs, an NLP startup operating in the financial domain, where he conducts research on LLMs, domain adaptation, text ranking, and more. He was the co-chair of the Privacy WG at BigScience, the chair at TMLS 2022 and TMLS NLP 2022 conferences, and is currently writing a book on Large Language Models.
WORKSHOP INFO
One day full of LLMs!
Fri Mar 1st, 9am - 5pm ET(times are in ET)