Deploying an LLM is the easy part. Keeping it reliable, accurate, and resilient in production is where teams get burned and most never see it coming.

This session is a practitioner's postmortem from building and operating end-to-end AI-enabled systems using GPT-4 and sentence transformers across Python and PHP stacks, from model training through API integration to server infrastructure.

What we'll cover:
- API key expiry silently taking down live features and how to design fallbacks that actually work
- Model accuracy drift in production: how to detect it before your users do
- The gap between dev behavior and 2am production behavior under real traffic
- Bridging Python ML pipelines with PHP production APIs
- What to monitor, what to automate, and what to just accept will break
- A practical pre-launch checklist for AI-integrated systems

This is real-world experience, not a framework walkthrough. No vendor demos. No slides that only work in theory.