Episode Summary:

In this episode of Engineering Choices You Have to Defend, host Nicola Onassis sits down with Keith Deming, an engineering leader with experience at Postmates, Uber, and PRISM Skylabs, to explore a pivotal architectural decision that transformed how computer vision systems scale in the real world.

At PRISM Skylabs, Keith and his team built a platform that turned retail surveillance cameras into powerful analytics tools, tracking foot traffic, customer journeys, and in-store engagement. The system worked exceptionally well… until customers wanted it everywhere. What started as a four-camera deployment quickly became a 200-camera scaling challenge, exposing the limits of server-based infrastructure.

Keith shares how the team faced mounting constraints, hardware costs, power consumption, cooling limitations, and physical space, and realized that simply scaling servers wasn’t viable. Instead, they made a bold shift: moving compute from centralized servers directly onto the cameras themselves.

The conversation dives into how a Raspberry Pi prototype proved edge computing was feasible, why rewriting performance-critical systems from Python to C++ became necessary, and how eliminating video decoding overhead unlocked real-time processing. More importantly, this architectural shift didn’t just solve a technical problem, it removed friction from the buying process, making it easier for customers to adopt and scale the product incrementally.

Keith also reflects on how modern advancements in edge AI and distributed computing are reshaping system design today, and why many teams still underestimate the true cost of centralized infrastructure.

For engineering leaders, this episode highlights a critical lesson: scaling isn’t always about adding more resources—it’s about rethinking where computation happens.

Key Takeaways:

• Centralized infrastructure can become the biggest bottleneck to scale
• Edge computing eliminates hardware, power, and space constraints
• Moving the compute closer to the data reduces latency and processing overhead
• Prototyping with simple tools (like Raspberry Pi) can unlock major breakthroughs
• Rewriting for performance (Python → C++) is often necessary at scale
• Removing infrastructure friction accelerates customer adoption
• The best architectures reduce reasons for customers to say “no”
• Distributed and edge-based systems are becoming the future of AI deployment

Connect with Keith Deming:

• LinkedIn: https://www.linkedin.com/in/keith-deming

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