Measurement-Before-Prediction Architecture: framework reference brief

Most infrastructure AI deployments fail because the measurement framework was not designed before the data was collected. This framework reverses that sequence, working backwards from the prediction to the data schema. A five-step sequence for designing the measurement schema before data collection begins, so AI predictions don't require months of recalibration after go-live. What this brief contains. The framework's reference diagram, the method and its application rule, and the citation record (version, first-published and last-revised dates). It is the printable form of the framework page at https://terencekok.com/frameworks/measurement/, which remains the maintained version. Provenance and status. First published on terencekok.com on 5 June 2026; this brief is version 1.1, revised 12 September 2026. A practitioner framework, not a peer-reviewed publication. Licensed CC BY-NC 4.0.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-12
DOI
https://doi.org/10.5281/zenodo.22722225
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
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article

Measurement-Before-Prediction Architecture: framework reference brief

Terence Kok
Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
article

Measurement-Before-Prediction Architecture: framework reference brief

Terence Kok
article en

Abstract

Most infrastructure AI deployments fail because the measurement framework was not designed before the data was collected. This framework reverses that sequence, working backwards from the prediction to the data schema. A five-step sequence for designing the measurement schema before data collection begins, so AI predictions don't require months of recalibration after go-live. What this brief contains. The framework's reference diagram, the method and its application rule, and the citation record (version, first-published and last-revised dates). It is the printable form of the framework page at https://terencekok.com/frameworks/measurement/, which remains the maintained version. Provenance and status. First published on terencekok.com on 5 June 2026; this brief is version 1.1, revised 12 September 2026. A practitioner framework, not a peer-reviewed publication. Licensed CC BY-NC 4.0.

Zenodo (CERN European Organization for Nuclear Research)
Golden Gate University (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 8%
Adversarial Robustness in Machine Learning
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