World Congress 2025 • Aug 20, 2025 • Session details

Reference Architecture of AI in the Cloud

Radu Vunvulea

AI isn't a standalone magic box. Bolting it onto outdated systems destroys performance. Learn to build the cloud-native reference architecture needed to scale AI workloads.

Pause
Mute Enter Fullscreen
#1 about 4 min

Why legacy cloud applications require updates for AI

Recognizing that existing cloud applications from different vendors may not be inherently ready to adopt and interact with AI workloads.

#2 about 4 min

Navigating AI service complexity and developer roles

The rapid proliferation of AI services across vendors continues to necessitate skilled application developers alongside data scientists.

#3 about 6 min

Overcoming challenges of cloud architecture for AI

Balancing auto-scaling limits, data consolidation, security compliance, and latency costs is critical when adding artificial intelligence capabilities.

#4 about 6 min

Critical success factors for application modernization

Key pillars for AI readiness include robust auto-scaling, unified data platforms, mature CI/CD pipelines, and deep system observability.

#5 about 5 min

Structuring compute and data services for AI models

Orchestrating Kubernetes for mixed compute capabilities, serverless functions for unpredictable payloads, and data catalogs for secure information discovery optimizes AI delivery.

#6 about 2 min

Building the supportive infrastructure around AI components

Providing the networking, application logic, and infrastructure surrounding core LLM outputs ensures holistic business functionality.

#7 about 3 min

Following a logical progression for cloud modernization

Transitioning legacy systems to AI-ready architectures requires stepping systematically through compute scaling, data consolidation, automation, and unified observability.

#8 about 2 min

Prioritizing event-based structures for AI architecture

Transforming foundational applications into strictly data-driven and event-based environments minimizes bottlenecks tied to inelastic infrastructure setups.

Matching moments

1:18 min

Simplifying AI deployments using architectural blueprints and reference implementations

Stephan Gillich Stephan Gillich · WWC 2024

3:08 min

Modernizing legacy infrastructure for ai driven business transformation

Kai Grunwitz Kai Grunwitz +2 · WWC 2025

2:46 min

Deploying AI agents for enterprise legacy code modernization

Neel Sundaresan Neel Sundaresan +1 · WWC Europe 2026

2:00 min

Reviewing key architectural decisions for federated AI computing

Jeremy Murray Jeremy Murray · WWC Europe 2026

1:43 min

Core foundations for successful artificial intelligence transformation

Alexander Birke Alexander Birke +1 · WWC 2024

3:52 min

Establishing a structured framework for enterprise AI

Julian Joseph · LIVE

Upcoming sessions on this topic

Open session

World Congress 2026 North America

You Can’t Re-Run Sunlight: Designing ML Data Architectures for Physical AI

An Phan

Senior Data Infrastructure Engineer @ Hippo Harvest

An Phan
Open session

World Congress 2026 North America

Reinventing Testing Practices in the AI Era

Eric Deandrea

Java Champion & Senior Principal Software Engineer, IBM

Eric Deandrea
Open session

World Congress 2026 North America

AI Decision Observability: Enabling Transparency and Trust in Intelligent Systems

Soumil Mandal, Amjad Shaikh

Soumil Mandal
Amjad Shaikh
Open session

World Congress 2026 North America

Building Stuff with GenAI - The Open Minded Workshop beyond OpenAI

Andreas Erben

CTO for Applied AI and Metaverse at daenet

Andreas Erben
Open session

World Congress 2026 North America

AI ROI: The Hard Unit Economics of AI-Native Engineering

Manu Gurudatha

Manu Gurudatha, VP of Engineering at PagerDuty

Manu Gurudatha
Open session

World Congress 2026 North America

AI Agents are Only as Smart as their Context: Building a Real-Time Context Engine at Intuit

Bharat Patel

Lead Software Engineer at Intuit

Bharat Patel