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From Lab to Production: Making AI Traffic Control Reality
15 min read4 days ago
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After building the core system in Part I and supercharging it with advanced algorithms in Part II, I faced what might be the hardest part of any machine learning project: making it actually deployable.
Research prototypes and production systems live in different worlds. My enhanced DQN agent was achieving impressive results in simulatio…
Press enter or click to view image in full size
Image by Intetra
Member-only story
From Lab to Production: Making AI Traffic Control Reality
15 min read4 days ago
–
After building the core system in Part I and supercharging it with advanced algorithms in Part II, I faced what might be the hardest part of any machine learning project: making it actually deployable.
Research prototypes and production systems live in different worlds. My enhanced DQN agent was achieving impressive results in simulation, but a 13 MB model that takes 100ms per inference won’t cut it when you need to control dozens of intersections in real-time on embedded hardware.
Let me paint you a picture of what “production-ready” actually means for traffic control systems.
You’re deploying to edge devices at each intersection — probably something like a Raspberry Pi 4 or similar embedded hardware. These devices have limited compute (no GPUs), constrained memory (4–8 GB), and need to make decisions in real-time while handling communication with central traffic management systems.
Your model needs to:
- Inference in under 50ms (20 FPS minimum)
- Fit in under 5–6 MB of storage
- Use minimal RAM (< 500 MB)
- Be robust to sensor failures and…