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Evaluations and datasets · Lesson 14 of 18 · 6 min

Review dataset provenance

Dataset provenance records origin, permission, transformations and version. Without it, a technically valid file may still be inappropriate or impossible to reproduce.

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learner@clairosSimulated AI Ops lab
learner@clairos:~ $
↑ history · Tab complete · Ctrl+C cancel · Esc, Tab to leaveExit 0

Understand the pattern

Dataset provenance records origin, permission, transformations and version. Without it, a technically valid file may still be inappropriate or impossible to reproduce.

Interpret the evidence

Resolve and record permission before approving this dataset for the intended use.

A tempting mistake

A valid JSON schema proves you have permission to use the data.

All names, measurements, prices, versions, and thresholds in this exercise are fictional training data. The evidence supports this bounded scenario, not a claim about a live provider.

Worked example and expected result
$ cat /opt/ai-lab/cases/aiops-evals-datasets-014.json
{
  "fixture": "Fictional training data; not a live measurement.",
  "source_recorded": true,
  "permission_recorded": false,
  "transform_version": "clean-v3"
}

Exit status: 0. The command completed successfully in the training fixture.

$ jq .permission_recorded /opt/ai-lab/cases/aiops-evals-datasets-014.json
false

Exit status: 0. The command completed successfully in the training fixture.

$ printf '%s\n' 'resolve-data-permission' > /home/learner/lab/aiops-evals-datasets-014-decision.txt
(no output; exit 0)

Exit status: 0. The command completed successfully in the training fixture.

Try a variation

Name the assumption that would have to change for “resolve-data-permission” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Review dataset provenance” justify?

Apply it elsewhere

A colleague concludes: “A valid JSON schema proves you have permission to use the data.” What should you do?

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Build a regression reportTry an incident drill

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Keep learning

  1. 01Detect evaluation leakageEvaluation leakage happens when held-out information influences tuning or selection. A contaminated test set can exaggerate generalization performance.Practice
  2. 02Build a regression reportA regression report identifies which cases worsened under a controlled comparison. Include the baseline and candidate versions and representative failures, not just a red badge.Practice
  3. 03Make a release recommendationA release recommendation is a decision against recorded gates. Separate evidence that passed, evidence that failed and any untested conditions.Practice