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 architecture from the beginning rather than something added after the model performs well offline.
For example, when working on production AI systems at Software Developers Pro, we’ve had to think about questions such as:
- How should model versions be evaluated before deployment?
- What should be monitored beyond basic latency and uptime?
- How do you handle degraded model performance or unexpected inputs?
- When is batching worth the additional complexity?
- How much of the inference pipeline should remain in PyTorch versus being moved into other serving components?
I’m curious how others here approach this transition. What has been the biggest difference between your PyTorch development workflow and your production deployment workflow?
Particularly interested in lessons around inference performance, monitoring, reproducibility, and model updates.