Vector databases are a type of database that use graph embeddings to represent and compare data, making them ideal for fuzzy match problems. Graph embeddings are created using machine learning algorithms and compress the attributes of data into a low-level representation. The process of creating a new embedding vector is called encoding.
Through their support for LLMs, vector databases are expected to make a dramatic impact on IT budgets this year. Join us for the speaker’s unique voice to the enterprise on this important subject.
About the Speaker
William McKnight
President, McKnight Consulting Group
William McKnight has advised many of the world’s best-known organizations. His strategies form the information management plan for leading companies in numerous industries. He is a prolific author and a popular keynote speaker and trainer. He has performed dozens of benchmarks on leading database, data lake, streaming, and data integration products. William is the #1 global influencer in data warehousing and master data management, and he leads McKnight Consulting Group, which has twice placed on the Inc. 5000 list.