Ryan is a Data Engineer at Zilliz working with Milvus, an open source vector database. He recently graduated from the University of California, Berkeley, with a B.A. in Data Science. At Zilliz, his areas of focus include leveraging public cloud infrastructure to power search and helping users achieve their deployment goals.
Ryan Chan is speaking at the following session/s
Milvus: Billionth-Scale Similarity Search in Milliseconds
Milvus is the world's most advanced open source vector database and similarity search engine, used by over 1,000 of the "who's-who" organizations around the world including Lucidworks! Developed as a cloud native scalable architecture that supports storage and search of billions of vectors, the core is built on top of state of the art ANN algorithm libraries such as FAISS, HNSW and Annoy. The session will showcase the power of Milvus along with highlighting the SDK availability for Python, JAVA, GO, and restful.
Developers, IT Management, Software Developers
Attendees will walk away with a thorough understanding of how vector databases can accelerate similarity search by 10x and also be utilized by organizations in multiple industries to query billionth-scale datasets in milliseconds. Attendees will learn about mainstream use-cases where Milvus is deployed in production, and learn how to take advantage of the software for their applications.