Objective: turn a trained model into a versioned, monitorable service.
A weights file is not a product. The system includes preprocessing, model, post-processing, threshold, input schema, dependencies and fallback rules.
request → validation → versioned preprocessing → model → post-processing
→ decision/abstention → logging → monitoringDefine types, shapes, units, missing values, maximum size, normalization, class order and version. Validate the input before computing. A silent class permutation can make a model numerically correct but operationally dangerous.
Version: weights, code, configuration, tokenizer or transforms, label mapping, metrics, split reference/data, dependencies and model card. A file fingerprint enables integrity checking.
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