Serving, containers, and resources · Lesson 1 of 17 · 7 min

Inspect the serving service

A serving process has liveness, readiness and dependency state. Inspect the service before restarting it so the intervention addresses a measured condition.

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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-serving-resources-001.json. Separate the recorded facts from your assumptions.
Hint & worked example
A serving process has liveness, readiness and dependency state. Inspect the service before restarting it so the intervention addresses a measured condition.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-serving-resources-001.json
```
Common mistake to avoid: An active service must already be ready for inference.
2
Isolate the deciding signal
Select the dependency field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Inspect the loading runtime rather than assuming the active gateway is ready for traffic.
A successful JSON query isolates dependency.
```
jq .dependency /opt/ai-lab/cases/aiops-serving-resources-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-loading-runtime, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-serving-resources-001-decision.txt.
Hint & worked example
Inspect the loading runtime rather than assuming the active gateway is ready for traffic.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'inspect-loading-runtime' > /home/learner/lab/aiops-serving-resources-001-decision.txt
```
Common mistake to avoid: An active service must already be ready for inference.

Understand the pattern

A serving process has liveness, readiness and dependency state. Inspect the service before restarting it so the intervention addresses a measured condition.

Interpret the evidence

Inspect the loading runtime rather than assuming the active gateway is ready for traffic.

A tempting mistake

An active service must already be ready for inference.

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-serving-resources-001.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "service": "ai-gateway",
  "active": "active",
  "ready": false,
  "dependency": "model-runtime loading"
}

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

$ jq .dependency /opt/ai-lab/cases/aiops-serving-resources-001.json

"model-runtime loading"

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

$ printf '%s\n' 'inspect-loading-runtime' > /home/learner/lab/aiops-serving-resources-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-loading-runtime” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Inspect the serving service” justify?Make the change now and gather the missing evidence afterward.Inspect the loading runtime rather than assuming the active gateway is ready for traffic.An active service must already be ready for inference.

Apply it elsewhere

A colleague concludes: “An active service must already be ready for inference.” 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 gateway logs Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision