I used to think computer vision was a model problem. It is a pipeline problem, and the model is the easy part.
Here is the pattern I keep seeing. A team trains or fine-tunes a detector, it hits some impressive number on a curated clip, everyone claps, and then it goes into the real world and falls over. The lighting is wrong. The camera moved. Someone wore a color the training set never saw. The response arrives half a second late and the whole thing feels broken. The demo worked. The product did not.
I have shipped enough camera-driven products now to believe the mental model most people start with is wrong. Computer vision is not "a model that recognizes things." It is a real-time loop: sense, decide, act, fast and reliable, in a world you do not control. The model is one node in that loop. Getting the model to work is table stakes. Getting the loop to hold up is the actual engineering.
The model is a node, not the product
If you have built any real-time system, this framing will feel familiar. Think of the vision model the way you think of a single service in a request path. It takes an input, produces an output, and has a latency cost. It is not the system. The system is everything around it: where the frames come from, what happens to the output, how fast the whole chain runs, and what the product does when the model is wrong (because it will be wrong).
