Everyone's shipping AI features. Few are talking about what happens after.

This is a practitioner's postmortem on building and running real AI-enabled systems end-to-end from training sentence transformer models to integrating GPT-(4,5) APIs to keeping the servers alive. No slides full of architecture diagrams that never see production. Just honest lessons from the trenches.

We'll cover:
- What happens when your OpenAI API key expires mid-request and your app has no fallback
- Model accuracy drift in the wild how to catch it before your users do
- The hidden operational gap between "it works in dev" and "it works at 2am"
- Lessons from bridging Python ML pipelines with PHP production APIs
- What to monitor, what to automate, and what to just accept will break

You'll leave with a practical checklist for hardening AI-integrated systems and a much healthier skepticism of demo-ware.