Models, artifacts, and runtimes · Lesson 1 of 16 · 7 min

List model inventory

A model inventory is an index of artifacts and their intended roles. Identify the active version and retained rollback separately; a listed artifact is not necessarily loaded.

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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-001.json. Separate the recorded facts from your assumptions.
Hint & worked example
A model inventory is an index of artifacts and their intended roles. Identify the active version and retained rollback separately; a listed artifact is not necessarily loaded.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-models-runtimes-001.json
```
Common mistake to avoid: Assume the highest-looking version is already serving traffic.
2
Isolate the deciding signal
Select the active field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Inspect m3 as the active artifact and retain m2 as rollback.
A successful JSON query isolates active.
```
jq .active /opt/ai-lab/cases/aiops-models-runtimes-001.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: inspect-active-m3, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-models-runtimes-001-decision.txt.
Hint & worked example
Inspect m3 as the active artifact and retain m2 as rollback.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'inspect-active-m3' > /home/learner/lab/aiops-models-runtimes-001-decision.txt
```
Common mistake to avoid: Assume the highest-looking version is already serving traffic.

Understand the pattern

A model inventory is an index of artifacts and their intended roles. Identify the active version and retained rollback separately; a listed artifact is not necessarily loaded.

Interpret the evidence

Inspect m3 as the active artifact and retain m2 as rollback.

A tempting mistake

Assume the highest-looking version is already serving traffic.

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-001.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "inventory": [
    "m2",
    "m3",
    "m4-candidate"
  ],
  "active": "m3",
  "rollback": "m2"
}

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

$ jq .active /opt/ai-lab/cases/aiops-models-runtimes-001.json

"m3"

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

$ printf '%s\n' 'inspect-active-m3' > /home/learner/lab/aiops-models-runtimes-001-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 “inspect-active-m3” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “List model inventory” justify?Make the change now and gather the missing evidence afterward.Inspect m3 as the active artifact and retain m2 as rollback.Assume the highest-looking version is already serving traffic.

Apply it elsewhere

A colleague concludes: “Assume the highest-looking version is already serving traffic.” What should you do?Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.Treat this fixture as a live measurement.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Read a model manifest Try an incident drill

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Case methodology: evidence, limits, decision