Inference requests and APIs · Lesson 1 of 16 · 8 min

Check gateway health

A health endpoint can describe process liveness without proving model readiness. Read the scope of the endpoint before using its status to approve traffic.

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-inference-apis-001.json. Separate the recorded facts from your assumptions.
Hint & worked example
A health endpoint can describe process liveness without proving model readiness. Read the scope of the endpoint before using its status to approve traffic.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-inference-apis-001.json
```
Common mistake to avoid: HTTP 200 on a liveness check proves inference is ready.
2
Isolate the deciding signal
Select the model_ready field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Keep inference traffic gated until model readiness is confirmed.
A successful JSON query isolates model_ready.
```
jq .model_ready /opt/ai-lab/cases/aiops-inference-apis-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: wait-for-readiness, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-inference-apis-001-decision.txt.
Hint & worked example
Keep inference traffic gated until model readiness is confirmed.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'wait-for-readiness' > /home/learner/lab/aiops-inference-apis-001-decision.txt
```
Common mistake to avoid: HTTP 200 on a liveness check proves inference is ready.

Understand the pattern

A health endpoint can describe process liveness without proving model readiness. Read the scope of the endpoint before using its status to approve traffic.

Interpret the evidence

Keep inference traffic gated until model readiness is confirmed.

A tempting mistake

HTTP 200 on a liveness check proves inference is ready.

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-inference-apis-001.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "http_status": 200,
  "gateway_alive": true,
  "model_ready": false
}

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

$ jq .model_ready /opt/ai-lab/cases/aiops-inference-apis-001.json

false

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

$ printf '%s\n' 'wait-for-readiness' > /home/learner/lab/aiops-inference-apis-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 “wait-for-readiness” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Check gateway health” justify?Make the change now and gather the missing evidence afterward.Keep inference traffic gated until model readiness is confirmed.HTTP 200 on a liveness check proves inference is ready.

Apply it elsewhere

A colleague concludes: “HTTP 200 on a liveness check proves inference is ready.” 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.

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Case methodology: evidence, limits, decision