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Why AI Prototypes Fail in Production and What Reliable Deployment Requires

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An AI application that performs well in a demo can still fail once deployed, because the model is only one part of a larger software system. Real-world conditions such as slow providers, traffic spikes, dependency conflicts, and database limits expose problems unrelated to model quality. The article argues that engineers must address containerization, failure boundaries, retries, observability, data security, and recovery from interruptions. It uses a document intelligence service as an example, showing how many external dependencies each add potential points of failure. The author's book, The AI Engineer Volume II, covers these practices for teams moving AI applications beyond prototypes.

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