Your model runs on the data scientist's laptop but breaks in production. How do you package it for reproducible deployment?
'Works on my machine' is a dependency and environment problem, and for ML it drags in CUDA versions and exact library pins that shift numerical results. Here is how to bring the production environment in line with training.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
'Works on my machine' is a dependency and environment problem, and for ML it drags in CUDA versions and exact library pins that shift numerical results. Here is how to bring the production environment in line with training.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
