Vector similarity search is powerful. It can extract valuable insights from complex information and make relevant recommendations from abstract inputs. But to be useful, it has to be fast. Whether suggesting products or surfacing valuable information, results often need to be served instantaneously.
How can you ensure similarity search is speedy, even when querying massive million, billion, or even trillion vector datasets? We've partnered with Lucidworks, an enterprise search technology company, to explain how the company used Milvus to build semantic search at speed. Join our webinar and discover how Milvus, an open-source vector database, makes it possible for Lucidworks to search an entire vector space in milliseconds.
What you will learn:
- Learn how Milvus helps power Lucidworks semantic search.
- Learn more about Lucidworks application and use of Milvus to Index and Query within applications.
- Learn how scale testing was run using the Milvus FLAT and HNSW indexes with demonstrated results.
- Q & A will be available after the presentation
AI Research Engineer at Lucidworks
Sava is an AI Research Engineer at Lucidworks, whose main focus is researching, developing and integrating cutting-edge NLP algorithms. As a part of the Data Science team, he has been working closely on question answering systems, chatbots and neural search engines as well as on enriching search results with additional information like sentiment, topics, extracted named entities etc. He's also actively participating in a variety of Machine Learning competitions on Kaggle and other platforms, where he was able to achieve winning places by applying state-of-the-art Deep Learning techniques.
Senior Engineer at Lucidworks
Elizabeth Edmiston has been a Senior Engineer at Lucidworks for the past three years. Prior to joining Lucidworks Elizabeth managed multiple big data projects. She has had almost thirty years of experience as developer, consultant, architect, manager, and educator and continues to pursue her love of data and solving problems. Elizabeth earned her Ph.D. in Computer Science from Duke University.
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