{"id":30590,"date":"2026-08-20T11:13:01","date_gmt":"2026-08-20T11:13:01","guid":{"rendered":"https:\/\/framos.com\/articles\/from-prototype-to-production-building-ai-at-the-edge\/"},"modified":"2026-08-21T11:22:34","modified_gmt":"2026-08-21T11:22:34","slug":"from-prototype-to-production-building-ai-at-the-edge","status":"publish","type":"articles","link":"https:\/\/framos.com\/de\/fachartikel\/from-prototype-to-production-building-ai-at-the-edge\/","title":{"rendered":"From Prototype to Production: Building AI at the Edge"},"content":{"rendered":"\n

An AI model that works on a development board is still a long way from a production-ready edge device. The final system must capture useful data, process it within a limited power budget, respond in real time, and remain reliable in the environment where it will operate.<\/p>\n\n\n\n

At ImagingNext 2026, Charles Naoum, Senior Director at Qualcomm Europe<\/a><\/strong>, presents \u201cFrom Prototype to Production: Building AI at the Edge<\/strong><\/a>\u201d The session explores the building blocks required to turn an early AI concept into a responsive, scalable, and deployment-ready industrial system.<\/p>\n\n\n\n

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About Charles Naoum<\/h2>\n\n\n\n

Charles Naoum<\/a> <\/strong>is Senior Sales Director Europe for Industrial and Embedded IoT at Qualcomm. He has more than 15 years of experience in the semiconductor industry, with previous commercial and business development roles at NXP Semiconductors, Broadcom, and Texas Instruments. His background combines electronic engineering and business management, with experience spanning embedded technologies, connectivity, industrial applications, and strategic customer development across Europe.<\/p>\n<\/div><\/div>\n\n\n\n

Why building AI at the edge is a system problem<\/strong><\/h3>\n\n\n\n

AI is changing how machines perceive, interpret, and respond across robotics, industrial inspection, smart cameras, and connected edge devices. These applications increasingly need to process information locally and react without sending every frame or sensor reading to the cloud.<\/p>\n\n\n\n

On-device processing can support faster responses and more practical deployment, but it also creates engineering constraints. Imaging quality affects the information available to the model. Compute performance influences which AI workloads can run in real time. Power consumption, memory, and thermal limits determine whether a prototype can become a viable product.<\/p>\n\n\n\n

Moving toward production introduces further requirements. The system must remain reliable, scale beyond a small number of prototypes, and support long-term deployment. Compute efficiency, thermal behavior, industrial hardware, and maintainability become as important as the performance of the AI model itself.<\/p>\n\n\n\n

The session will examine the role of Qualcomm Dragonwing in enabling AI processing and power-efficient compute at the edge. It will also show how tools and ecosystems such as Arduino and Edge Impulse can simplify prototyping and model development, helping teams move from an early concept toward production-ready deployment.<\/p>\n\n\n\n

The focus is on the complete edge AI platform rather than a single component. Imaging, compute, software, development tools, and industrial design decisions must work together if the final system is to perform reliably in a real-world application.<\/p>\n\n\n\n

What you’ll take away<\/strong><\/h3>\n\n\n\n