In this episode of Digital Transformation Architect, Dr. Darren discusses how organizations can make thoughtful AI architecture decisions by focusing on real workloads and measurable outcomes rather than just model selection. He explains how to match AI models to deployment locations—device, edge, data center, cloud, or hybrid—based on factors like data sensitivity, latency, cost, and control. The episode also covers common AI architecture patterns, the importance of embedding trust and controls in the design, measuring cost per useful outcome, and stress-testing the architecture before deployment. Listeners are encouraged to use the AI Workload Decision Canvas to map workloads, surface assumptions, and prioritize actions toward scalable, trustworthy AI solutions.
In this episode of Digital Transformation Architect, Dr. Darren discusses how organizations can make thoughtful AI architecture decisions by focusing on real workloads and measurable outcomes rather than just model selection. He explains how to match AI models to deployment locations—device, edge, data center, cloud, or hybrid—based on factors like data sensitivity, latency, cost, and control. The episode also covers common AI architecture patterns, the importance of embedding trust and controls in the design, measuring cost per useful outcome, and stress-testing the architecture before deployment. Listeners are encouraged to use the AI Workload Decision Canvas to map workloads, surface assumptions, and prioritize actions toward scalable, trustworthy AI solutions.
