Deploying and Launching Your AI-Powered App

Your AI app’s biggest risk isn’t bad code—it’s a perfect launch that silently breaks a week later. One tiny model update, and suddenly recommendations feel “off,” support tickets spike, and nobody can explain why. This episode is about preventing that slow-motion crash.

Most AI apps don’t die in a dramatic outage—they quietly bleed out through tiny, unnoticed failures in how they’re deployed, released, and monitored. Last episode, we stopped at the moment where things “felt off.” Now we’re going further upstream: how do you launch in a way that makes those weird shifts hard to miss and easy to fix?

In this episode, we’ll zoom in on three parts of a healthy launch: the pipeline that gets your model from notebook to production without manual heroics, the way you expose new versions to real users without gambling your whole audience, and the telemetry that tells you when reality is starting to drift.