GAJENDRA BABU THOKALA

Title of the Talk:
From Batch to Always-On: Rethinking Data Architecture for AI at Scale

Abstract:
The Data Architecture Gap Facing AI – AI’s growth has surpassed its original data architecture. Systems built primarily for reporting on a periodical basis and overnight batch processes are currently being used to generate real-time actionable intelligence that needs to respond to changing conditions within seconds. This has created a growing divide between what organizations expect their AI applications to accomplish versus what the current data platform technology supports.

In this Keynote presentation we will discuss the existing technology divide; evaluate how decisions regarding data ingestion, processing and coordination affect the response time and reliability of AI enabled systems and explain why moving from batch-based designs to continuous always-on, event driven architectures is no longer an option for those working at high volume. We will compare legacy data architecture technologies with modern ones to illustrate how continuously operating data platforms turn delayed insights into real-time operational intelligence. Audience members will be able to understand better the design principles, trade-offs and examples of how to build scalable data platforms supporting AI based solutions.

Bio:
Gajendra Babu Thokala is an engineering leader who specializes in creating very large scale data engineering, real-time (streaming) systems, and artificial intelligence driven platforms. Throughout his professional experience in India, Singapore and the U.S., he has been responsible for designing and scaling extremely complex, high throughput systems to support the reliable operation of intelligent applications at global scales.
Gajendra’s work spans both industrial practices and applied research areas and is focused primarily on the architectural underpinnings necessary for building resilient, data-driven, and intelligent systems. Gajendra Babu Thokala is an IEEE Senior Member, a BCS Fellow, and a published author. His role as a peer-reviewer and keynoter at international conferences and events has contributed to discussions related to modern data platforms and real-time Artificial Intelligence.
He is well-known for converting highly technical problems into practical, scalable platform solutions; and providing practical insights to assist engineers/technology leaders develop the next-generation of trust-worthy real-time systems.

.