Jun Gu is the partner of Zilliz, performing the Senior Architect role. Before joining Zilliz, Jun received his undergraduate degree in Computer Science from Peking University and worked as a database technician for 14 years in companies like ICBC, IBM, Morgan Stanley, and Huawei. Jun is a PMC member of the Milvus project, an LF AI & Data incubation project. Jun has delivered several speeches on the Milvus project in different OSS summits hosted by the Linux Foundation.
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How to accelerate approximate nearest neighborhood search (ANNS) for large scale datasets.
Introduction (Jun’s background, basic info on Zilliz) - 0:00
How to unlock the treasure of unstructured data (Jun describes the challenge of understanding and utilizing unstructured data) - 2:21
The flow-based AI applications (Jun talks about the most popular way of using AI technologies to analyze and structure data and the problem of data fragmentation that comes with it) - 4:03
The unstructured data service for AI (A more complex model for dealing with unstructured data and how Milvus fits into it) - 6:10
Vectors are different (Why traditional databases don’t meet the requirements of vector analysis and similarity comparison, segue into what Milvus does differently) - 11:12
Milvus: the big picture (A diagram describing different parts of Milvus project, support for different application development environments) - 13:26
The ANN benchmark (Jun talks about performance comparison and explains how Milvus manages to achieve faster search with lower memory consumption) - 16:08
Data management (Technical explanation of how Milvus does data management, what changed between different versions of Milvus) - 21:44
Our journey (A short story about Milvus and how it became one of the most active AI projects in Linux foundation, basic facts: number of users, releases, patents etc.) - 28:43
User scenarios (Jun walks us through three real world use cases: an intelligent writing assistant, image search for company trademark and pharmaceutical molecule analysis) - 30:07
Places to connect and read more about Milvus - 35:22
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