Designing an Image Recognition Pipeline from Inference to Audit Trail
A practical walkthrough for designing a full image recognition system from ingestion to auditability.
Machine learning systems become operational systems the moment a prediction changes a real workflow. That means ingestion, model execution, human review, and evidence retention all matter as much as the model itself.
A production-safe image pipeline should separate the raw asset store, the inference queue, the moderation or review step, and the audit log that records which model version produced which decision.