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Walk through an end-to-end computer vision pipeline from raw images to a deployed model.

Most CV systems fail at the seams: a preprocessing mismatch between training and serving, not the model. The signal is naming every stage from ingestion to serving and the one consistency invariant that breaks teams. Here is the answer.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

Most CV systems fail at the seams: a preprocessing mismatch between training and serving, not the model. The signal is naming every stage from ingestion to serving and the one consistency invariant that breaks teams. Here is the answer.

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Next in this trackHow do you choose loss functions for computer vision tasks (classification, detection, segmentation)?Next in this trackExplain semantic vs instance segmentation and how Mask R-CNN works.Next in this trackFor a dataset with a million points, would you use a deep network or KNN, and why?
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