Deep Learning Vector Search Service

Jeffrey Zhu and Mingqin Li, Microsoft

Abstract: 

Over the last couple of years, search has evolved beyond simple keyword-based information retrieval to more complex scenarios, such as natural language queries, Question-and-Answer, and multimedia search. Deep learning models are used to encode user intent and context into vector representations, which are then searched against billions of other vectors to find the most relevant results.

Deep Learning Vector Search Service (DLVS) is a low latency, large scale, and highly efficient vector search system at Microsoft, primarily used within the Bing search engine. This talk will discuss the key innovations in approximate nearest neighbor (ANN) algorithm and distributed vector index serving platform necessary to achieve this scale and performance.

Jeffrey Zhu, Microsoft

Jeffrey Zhu is a program manager at Microsoft who drives the development of Bing's deep learning platform. This platform powers some of Bing's most innovative features, such as machine reading comprehension and visual search. It serves millions of deep learning model inferences per second and supports vector search over billions of vectors at low latency and high efficiency.

Mingqin Li, Microsoft

Mingqin Li is the software engineering manager at Microsoft, who leads Bing's deep learning platform. Low latency, large scale, and highly efficient deep learning vector search service are developed for various scenarios like web search, similar image search, question-and-answering, etc. She is also one of the key contributors to open source project SPTAG, which published the approximate nearest neighbor (ANN) algorithm used in vector search.

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BibTeX
@conference {232937,
author = {Jeffrey Zhu and Mingqin Li},
title = {Deep Learning Vector Search Service},
year = {2019},
address = {Santa Clara, CA},
publisher = {{USENIX} Association},
}