Walk through an end-to-end computer vision pipeline from raw images to a deployed model.
Most CV systems break at the seams: a preprocessing mismatch between training and serving, not the model itself. The signal is naming every stage from ingestion through serving and the single consistency invariant that trips teams up. Here is the answer.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Most CV systems break at the seams: a preprocessing mismatch between training and serving, not the model itself. The signal is naming every stage from ingestion through serving and the single consistency invariant that trips teams up. Here is the answer.
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.
