Searching big data is complex. Traditional keyword-based search methods are poorly suited for processing and analyzing unstructured data such as images, text, video, and audio that do not follow a predefined model. Vector similarity search utilizes deep learning methods and ANN (approximate nearest neighbor) algorithms to provide faster, more accurate search capable of processing trillion-scale unstructured datasets. This talk focuses on vector indexing, which plays a major role in accelerating time-consuming queries on large datasets. We will cover how various index types can be used in Milvus, a popular open-source vector similarity search engine, as well as Milvus v1.0 features and future plans for the platform.
Dr. Xiaomeng Yi
Senior Researcher at Zilliz
Dr. Xiaomeng Yi graduated from Huazhong University of Science and Technology with a doctoral degree in computer architecture. His main field of research is vector similarity seach algorithms and distributed systems resource scheduling. Dr. Yi's research has been published in respected journals and at top conferences including IEEE Network Magazine, IEEE/ACM Transactions on Networking, IEEE International Conference on Distributed Computing, and ACM Transactions on Modeling and Performance Evalution of Computing Systems. Prior to joining Zilliz, Yi worked in Huawei's public cloud services division designing and optimizing virtual machine resource scheduling algorithms. Shiyu Chen
Zilliz Data Engineer/Developer Advocate
Shiyu graduated from Xidian University with a bachelor's degree in computer science. Ever since she joined Zilliz, Shiyu has been developing various Milvus solutions and faciliting onboard experience for new users. She is dedicated to the integration of Milvus into different sorts AI applications by guiding first-time users through the deployment of Milvus and helping them unleash the potential of vector similarity search.
What is vector similarity search? How to get a competitive edge using vector indexing?
Webinar recording Part 1
Webinar recording Part 2
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