What is the future of vector search? A fireside chat with HNSW author Yury Malkov
Zilliz Webinar - Zoom
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About the Session
The Hierarchical Navigable Small World (HNSW) is one of the most popular graph-based indices for nearest neighbor search. Join us for a fireside chat with Yury Malkov, as we discuss how he ended up developing this critical method of high-dimensional data analysis. We’ll explore how he got started in computer science and where he sees the future of vector search and LLMs. You’ll even get a chance to ask Yury questions at the end of the chat, you won’t want to miss it!
- Why HNSW and other graph indexes are great tools for vector search
- What is the future of vector search?
- Thoughts on the proliferation and increasing importance of LLMs as it relates to vector data
Meet the Speaker
Join the session for live Q&A with the speaker
Distinguished Software Engineer at VerSe InnovationYury Malkov, a Distinguished Engineer at VerSe Innovation, is currently developing crucial recommender systems that serve 100M+ users. His published research, boasting 2,000+ citations, spans CV, LLMs, RecSys, and Similarity Search, featuring widely recognized open-source solutions like HNSW and Learnable Triangulation. Yury's professional interests encompass constructing highly scalable content machine learning systems.