AI systems foundations · Lesson 6 of 15 · 5 min

Distinguish training and inference

Training updates learned parameters; inference uses a selected artifact to generate output. Generation settings can change inference behavior without changing the weights.

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-systems-foundations-006.json. Separate the recorded facts from your assumptions.
Hint & worked example
Training updates learned parameters; inference uses a selected artifact to generate output. Generation settings can change inference behavior without changing the weights.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-systems-foundations-006.json
```
Common mistake to avoid: A temperature change means the model was retrained.
2
Isolate the deciding signal
Select the weights_changed field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Classify this as inference configuration, not a training run.
A successful JSON query isolates weights_changed.
```
jq .weights_changed /opt/ai-lab/cases/aiops-systems-foundations-006.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: classify-inference, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-systems-foundations-006-decision.txt.
Hint & worked example
Classify this as inference configuration, not a training run.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'classify-inference' > /home/learner/lab/aiops-systems-foundations-006-decision.txt
```
Common mistake to avoid: A temperature change means the model was retrained.

Understand the pattern

Training updates learned parameters; inference uses a selected artifact to generate output. Generation settings can change inference behavior without changing the weights.

Interpret the evidence

Classify this as inference configuration, not a training run.

A tempting mistake

A temperature change means the model was retrained.

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-systems-foundations-006.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "operation": "generate",
  "weights_changed": false,
  "temperature_changed": true
}

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

$ jq .weights_changed /opt/ai-lab/cases/aiops-systems-foundations-006.json

false

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

$ printf '%s\n' 'classify-inference' > /home/learner/lab/aiops-systems-foundations-006-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 “classify-inference” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Distinguish training and inference” justify?Classify this as inference configuration, not a training run.A temperature change means the model was retrained.Make the change now and gather the missing evidence afterward.

Apply it elsewhere

A colleague concludes: “A temperature change means the model was retrained.” What should you do?Accept it because the training command completed.Treat this fixture as a live measurement.Reject that conclusion; return to the case evidence and its stated limits.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Identify deterministic components Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision