Production AI Systems
AI rarely fails because the model is bad — it fails in the last mile. This series maps the gap between prototype and production: messy data, unclear ownership, trust, rollback, and everything that kills AI in the real world.
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The prototype-to-production gap is not a model problem — it is a systems problem. Here are the four layers where AI deployments break after the demo looks great, and what to do about each one.
The bottleneck in AI has moved from building to deploying. The engineers who win in this era are not the ones who write the best code — they are the ones who close the gap between a working model and a working system in a customer's hands.