Paper·July 10, 2026
From notebook to production: what usually breaks
A practical checklist for taking ML experiments into systems that survive real traffic, cost pressure, and messy data.
Most demos die the first week they meet production. Not because the model is useless, but because the surrounding system was never designed.
What breaks first
- Data drift that nobody measured
- Latency budgets that only worked on a laptop
- Cost that looked fine at 100 requests and painful at 100,000
- Fallbacks that do not exist when the provider or GPU fails
A production-minded baseline
- Define the decision the model must support.
- Freeze an evaluation set that mirrors real documents or queries.
- Ship serving with monitoring before you polish the UI.
- Keep a boring fallback path for degraded mode.
If you only remember one thing: treat the model as a component inside a product, not the product itself.
MLOpsproductionLLM