This lecture examines how machine learning models evolve into fully operational distributed AI systems. It introduces the architectural components that enable scalable deployment, including data infrastructure, model serving, orchestration platforms, monitoring, governance, and cost control. The session further explores LLM-first and agentic architectures, highlighting how large language models integrate memory, tool invocation, and multi-step reasoning. Through a systems-oriented lens, students learn to evaluate reliability, feedback loops, and economic trade-offs, preparing them for contemporary roles in AI engineering and intelligent systems design.

Narayanan Jayaratchagan is a seasoned technology leader and Chief Architect with more than 27 years of experience designing and scaling enterprise-grade Data, Analytics, and AI platforms across industries. He currently serves as Associate Partner at Tiger Analytics, working at the intersection of software architecture, AI-native application design, and AI economics. He is widely recognized for his thought leadership on building cost-aware, pricing-aware, and economically sustainable AI systems. He is the author of the published book Elegant Software Design Principles and is currently working on AI Economics in Practice, where he introduces frameworks such as Total Cost of Intelligence, pricing-aware architectures, and AI-native design principles. He regularly blogs on AI-native systems, AI monetization, and the evolving role of architects in the intelligence era and is a frequent speaker at industry forums and academic institutions.

Sundaresan Narasimhamoorthy is seasoned Data Engineering leader with more than 27 years of experience in the industry. He is a Partner, Technology Consulting at Tiger Analytics, Chennai from 2021 and he was associated with Cognizant Technology solution in AI & Analytics practice before joining Tiger Analytics. He has extensive experience steering Fortune 500 clients on various areas like consulting, program delivery & governance. Possesses a combination of technical mastery in Modern Data Engineering, BI, GenAI and drives P&L, pre-sales, and execute complex, global program delivery.

This talk is mandatory for students pursuing Major/Minor in Data Science.