duo-ml-in-production
Decide where a learned model beats a hand-written rule tree, judge it on a metric that moves where users actually care, and run a generation pipeline that does not quietly produce errors at scale — ranking noisy queues instead of filtering them, harvesting training labels from the human review step, calibrating thresholds per skill level, resolving identity before trusting any funnel number, and staging rollout as a published coverage curve. Use when replacing years of accumulated branching logic with one model, choosing an offline metric, triaging user reports, building an item bank or a content generator, or asking why your new-user numbers look wrong. Triggers on phrases like should this be a model or rules, ranking model, AUC, which metric should we optimize, cold start for a new segment, candidate generation, generated content at scale, identity resolution, gradual model rollout.
- Version
- 2.0.0
- License
- MIT
Pinned to revision 78072c9528fb, so it is the text this page describes rather than whatever the author pushed since.
Files
- skills/duo-ml-in-production/SKILL.md
- skills/duo-ml-in-production/references/calibrate-the-same-measurement-per-skill-level.md
- skills/duo-ml-in-production/references/generate-from-one-parameterized-primitive.md
- skills/duo-ml-in-production/references/integrity-by-construction-not-secrecy.md
- skills/duo-ml-in-production/references/pair-every-generator-with-an-inspector.md
- skills/duo-ml-in-production/references/pick-the-metric-sensitive-where-users-care.md
- skills/duo-ml-in-production/references/rank-the-noise-dont-filter-it.md
- skills/duo-ml-in-production/references/replace-the-rule-tree-with-one-learned-decision.md
- skills/duo-ml-in-production/references/resolve-identity-before-you-trust-the-funnel.md
- skills/duo-ml-in-production/references/shallow-features-let-new-segments-inherit-performance.md
- skills/duo-ml-in-production/references/ship-coverage-as-the-progress-metric.md
- skills/duo-ml-in-production/references/shrink-the-candidate-set-instead-of-speeding-the-search.md
- skills/duo-ml-in-production/references/surprise-requires-a-model-of-the-expected.md
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