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Models, artifacts, and runtimes · Lesson 14 of 16 · 5 min

Review model provenance

Provenance connects an artifact to its source, license and revision. Preserve that chain alongside the hash so another operator can explain what was activated.

Open the labCourse map
Mastery 0/3Run itExplain itApply it
learner@clairosSimulated AI Ops lab
learner@clairos:~ $
↑ history · Tab complete · Ctrl+C cancel · Esc, Tab to leaveExit 0

Understand the pattern

Provenance connects an artifact to its source, license and revision. Preserve that chain alongside the hash so another operator can explain what was activated.

Interpret the evidence

Complete the missing license/provenance record before approving use.

A tempting mistake

A checksum replaces all origin and usage information.

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-models-runtimes-014.json
{
  "fixture": "Fictional training data; not a live measurement.",
  "source": "training-registry",
  "revision": "m3",
  "license_recorded": false,
  "hash_recorded": true
}

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

$ jq .license_recorded /opt/ai-lab/cases/aiops-models-runtimes-014.json
false

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

$ printf '%s\n' 'complete-provenance' > /home/learner/lab/aiops-models-runtimes-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 “complete-provenance” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

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

Apply it elsewhere

A colleague concludes: “A checksum replaces all origin and usage information.” What should you do?

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Build a model baselineTry an incident drill

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Keep learning

  1. 01Choose a rollback artifactA rollback artifact should be known-good, retained and compatible with the current runtime and configuration. The oldest available file is not automatically a safe rollback.Practice
  2. 02Build a model baselineA serving baseline includes model, tokenizer, runtime, quantization and workload. Record these together before tuning so changes can be attributed rather than guessed.Practice
  3. 03Models and runtimes checkpointModel release readiness combines compatibility, capacity and quality evidence. Passing one dimension is not permission to ignore another failed gate.Practice

Case methodology: evidence, limits, decision