What changes when a PyTorch prototype has to run reliably in production?

I’ve been thinking about the gap between getting a PyTorch model working in a notebook and actually operating it as part of a production application. In projects I’ve worked around, the model itself is often only one part of the problem. Data pipelines, inference latency, monitoring, model versioning, failure handling, cost, and integration with the rest of the application can become just as important. One approach I’ve found useful is to treat deployment requirements as part of the ML archite...