tradeoffs

The most role-defining judgment call in Applied AI: a shiny benchmark number against the operational price of a system nobody can debug at 2am. Most candidates ship on the number and lose the loop there. Here is the answer that passes.

Federated learning keeps data on-device, but it costs you accuracy, debuggability, and engineering complexity. The signal is naming when those costs are justified and when a simpler centralized pipeline with DP wins. Here is the answer.

Every AI team feels the pull between velocity and doing it responsibly. How you navigate that tension, with judgment rather than dogma in either direction, is what this question screens. Here is the answer.