Models, artifacts, and runtimes · Lesson 3 of 16 · 9 min

Compare model versions

A version comparison must hold the task and evaluation inputs constant. A better aggregate result from another dataset is not a controlled improvement over the current model.

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learner@clairos:~ $

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-models-runtimes-003.json. Separate the recorded facts from your assumptions.
Hint & worked example
A version comparison must hold the task and evaluation inputs constant. A better aggregate result from another dataset is not a controlled improvement over the current model.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-models-runtimes-003.json
```
Common mistake to avoid: Promote m4 from scores measured on different datasets.
2
Isolate the deciding signal
Select the same_dataset field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Rerun both artifacts on the same held-out dataset before comparing quality.
A successful JSON query isolates same_dataset.
```
jq .same_dataset /opt/ai-lab/cases/aiops-models-runtimes-003.json
```
Common mistake to avoid: Using a different case or an unrelated signal as the justification.
3
Record your operational decision
Choose the justified code: rerun-comparison, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-models-runtimes-003-decision.txt.
Hint & worked example
Rerun both artifacts on the same held-out dataset before comparing quality.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'rerun-comparison' > /home/learner/lab/aiops-models-runtimes-003-decision.txt
```
Common mistake to avoid: Promote m4 from scores measured on different datasets.

Understand the pattern

A version comparison must hold the task and evaluation inputs constant. A better aggregate result from another dataset is not a controlled improvement over the current model.

Interpret the evidence

Rerun both artifacts on the same held-out dataset before comparing quality.

A tempting mistake

Promote m4 from scores measured on different datasets.

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-003.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "baseline_model": "m3",
  "candidate_model": "m4",
  "same_dataset": false
}

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

$ jq .same_dataset /opt/ai-lab/cases/aiops-models-runtimes-003.json

false

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

$ printf '%s\n' 'rerun-comparison' > /home/learner/lab/aiops-models-runtimes-003-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 “rerun-comparison” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Compare model versions” justify?Promote m4 from scores measured on different datasets.Make the change now and gather the missing evidence afterward.Rerun both artifacts on the same held-out dataset before comparing quality.

Apply it elsewhere

A colleague concludes: “Promote m4 from scores measured on different datasets.” What should you do?Treat this fixture as a live measurement.Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Inspect artifact metadata Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision