The difference between isolated pilots and sustainable AI at scale lies in the foundational architecture that supports data, governance, platforms, and operating models. In this deep-dive session, we explore how to architect an AI-enabled organization, with practical, hands-on architecture design. The session will begin with a concise overview of AI foundations, covering key AI building blocks such as model lifecycles, inference patterns, orchestration, observability, and responsible AI considerations. We then dive into key data concepts like modern data platforms, data products, metadata, data quality, lineage, and governance patterns required to reliably fuel AI workloads. Finally, the we’ll break into small groups to work through a real-world AI and Data architecture use case, using a provided case study and architecture toolkit. Each group will design a fit-for-purpose target architecture, making trade-offs across data platforms, AI services, integration patterns, governance, and scalability.

Key Takeaways:

• Understand the core architectural foundations required to operationalize AI at scale.
• Understand how data architecture and AI architecture must evolve together.
• Gain hands-on experience designing an enterprise-grade AI + Data architecture.