Vector Search and Adaptive AI and Understanding Java Classloaders

This session explores the application of vector search in mobile to create personalized, adaptive AI applications.
The talk gives an overview of Vector Search, and will cover strategies for efficient AI deployment powered by vector search on edge/mobile devices, addressing the challenges of latency and maximizing responsiveness without compromising on performance. The talk is thus aimed towards developers building mobile apps and willing to integrate adaptive AI capabilities in their apps.

Bio:
Shivay Lamba is a software developer specializing in DevOps, Machine Learning and Full Stack Development. He is currently a Developer Evangelist at Couchbase
He is an Open Source Enthusiast and has been part of various programs like Google Code In and Google Summer of Code as a Mentor and has also been a MLH Fellow. He is actively involved in community work as well. He is a TensorflowJS SIG member, Mentor in OpenMined and CNCF Service Mesh Community, SODA Foundation and has given talks at various conferences like Github Satellite, Voice Global, Fossasia Tech Summit, TensorflowJS Show & Tell.

Understanding Java Classloaders
During this session, we will try to discover the internals of classloaders and how to prevent a leak in Metaspace.

Speaker Bio
Fairoz Matte works in Oracle as JVM Sustaining Engineer, part of Java platform group. In day to day activity involves working on customer issues, with crash reports core dumps and trying to find the root cause, and provide fix for such crashes.