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Developer Ships Broken AI Model After Release Gate Validates Wrong File

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A machine learning developer discovered that a newly trained NLP model, despite scoring higher on benchmarks, shipped in a completely broken state — predicting 'neutral' for every input. The issue stemmed from a mismatch between the validated model and the actual quantized ONNX artifact uploaded for distribution. When the deployed file was tested directly against 24 stratified evaluation pairs, it returned only a single label regardless of input, yielding a macro F1 score of just 0.167. The developer traced the failure to the export and quantization pipeline, not the model training itself, meaning standard release checks never caught the defect. The incident highlights a critical gap in ML deployment practices: release gates must validate the exact file users download, not just the upstream model weights.

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