Data & AIData & AI
Conference50min
INTERMEDIATE

Making Sense of Vector Databases

This talk introduces the fundamentals of vector databases, explaining vectors, embeddings, indexing, and similarity metrics through progressively complex examples. It shows how vector databases enable applications like semantic search, recommendations, and anomaly detection, providing a clear, engaging understanding of their concepts and practical power.

talk.summaryAiDisclaimer

Balkrishna Rawool
Balkrishna RawoolING Bank

talkDetail.whenAndWhere

Friday, June 19, 13:35-14:25
Room 4B
talks.roomOccupancytalks.noOccupancyInfo
talks.description
Vector databases are quite a hot topic these days. From powering semantic search to recommendation systems and from anomaly detection to clustering, all of this can be achieved with vector databases. But how do they do this? Join this talk to find out.

We start with understanding what a vector is and create a simple example of how vectors can be used. Then we discuss new concepts around vectors and make the examples slightly more complex each time we add a new concept to it. Finally taking the examples to an incredibly amazing level!

All in all, this talk will explain what vectors are, how are they stored, what are embedding models, indexing algorithms and similarity/distance metrics with the help of interesting examples with increasing complexity that keeps you intrigued!
vectors
similarity
embeddings
indexing
talks.speakers
Balkrishna Rawool

Balkrishna Rawool

ING Bank

Netherlands

Balkrishna works as an engineering lead at ING Bank. He is a frequent speaker at renowned tech conferences. He has passion for continuous learning and genuine desire to sharing knowledge. Although he has been working with Java for many years, he finds latest developments in Java quite exciting. He is also an Oracle ACE Associate for Java.