Conformal Prediction for Industrial Quality Control: Coverage Guarantees Under Distribution Shift
DOI:
https://doi.org/10.5281/zenodo.22657207Keywords:
Conformal Prediction for Industrial Quality Control; Applied machine learning; Evidence synthesis; Production deployment; MLOpsAbstract
Conformal Prediction for Industrial Quality Control is an area of active development in applied machine learning, with evidence
that has accumulated unevenly across its component questions. We review the current evidence base for conformal prediction for
industrial quality control, spanning methodological, implementation, governance, and cost dimensions, focusing on where
practice has converged and where it continues to vary, and set out the practical considerations relevant to a team considering its
adoption. We conclude that selective, context-specific use is best supported by the evidence currently available, rather than
uniform adoption or avoidance.
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