Serving, containers, and resources

Inspect the serving service

Inspect the serving service through a bounded evidence-first AI operations workflow in the training lab.

Serving, containers, and resources
7 min Admin Lesson 108 of 180
cat /opt/ai-lab/deploy/ai-gateway.yamlaiops models listdocker ps
Lesson 108 of 180 0/180 lessons 0/18 missions 0/11 briefings Serving, containers, and resources · 7 min
0%
Continue View profile
AI operations terminal Training lab
New learnerrank 0XP 0%complete
learner@aiops:/home/learner $ AI operations lab: type a command, press Enter
Instructions 7 min

Click any instruction for the command details, the why, and the common mistake to avoid.

Inspect the serving, containers, and resources baseline

Type this exactly: cat /opt/ai-lab/deploy/ai-gateway.yaml

cat /opt/ai-lab/deploy/ai-gateway.yaml
Run the models review

Type this exactly: aiops models list

aiops models list
Confirm the evidence

Type this exactly: docker ps

docker ps
Lesson support

What to notice while you play.

Objective

Use commands and observable output to explain inspect the serving service without changing a real model or service.

Hint

Start with cat /opt/ai-lab/deploy/ai-gateway.yaml. Then run aiops models list before collecting the final evidence.

Why it matters

Inspect the serving service is an operator skill because AI behavior must be connected to versioned configuration, runtime state, and inspectable evidence.

Common mistakes
  • Skipping the baseline fixture before reasoning about inspect the serving service.
  • Treating one simulated output as proof of root cause instead of one bounded piece of evidence.
Reference

Commands in this lesson.

aiops models

Inspect the simulated model catalog, manifests, and version comparisons.

aiops prompts

Review versioned prompt metadata and deterministic prompt diffs.

aiops evals

Run and inspect bounded evaluation fixtures without model execution.

aiops rag

Inspect retrieval documents, chunks, index status, and reviewed results.

aiops traces

Read deterministic request traces, summaries, and latency evidence.

aiops guardrails

Review simulated guardrail policies, checks, and audit summaries.

aiops cost

Estimate cost and capacity from fixed training metrics.

aiops incidents

Review incident timelines, evidence bundles, and operator notes.

docker

Inspect fixed container, log, stats, and compose fixtures; execution and pulls are blocked.