Computer Vision in Production: Lessons from PyTorch Deployments

The gap between a notebook demo and a production computer vision system is almost always about the data distribution shift, not the model architecture. Lighting, camera angle, occlusion, and seasonal changes will find every blind spot in your training set.

We've found the highest-leverage investment isn't a bigger model — it's a tight feedback loop: sampling real production frames the model got wrong, relabeling them, and retraining on a regular cadence. Multi-model pipelines (a fast detector feeding a slower, more accurate classifier) often outperform one large model on both cost and accuracy.

On the infrastructure side: batch inference where you can, quantize models for edge deployment when latency matters, and always version your model artifacts alongside the exact preprocessing pipeline they were trained with — mismatches here are a common, quiet source of production bugs.