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The operational discipline for keeping generative AI systems reliable, safe, and cost-controlled after they go live.

The operational discipline that turns a working machine learning model into a system that keeps working in production — reliably, safely, and under governance.

The step where a trained model stops being a proof of concept and starts affecting real decisions — and where most AI projects either succeed or quietly fail.

How teams determine whether a model actually works — and the reason 'it works in testing' is often the most dangerous thing anyone says before launch.

Watching a live AI system for signs that it's silently getting worse — because models degrade in production without anyone noticing until something breaks.