LLMOps & AI Platform Engineering · Module 16 of 16
Production AI Platform Capstone
You are now the on-call engineer for a shared AI platform. The job is not to click the most dramatic button. Read the evidence, identify the failure domain, choose the lowest-risk action, verify recovery and leave an auditable explanation.
Engineering focus: Production AI platform control-room capstone
In plain English
What problem are we solving?
You are now the on-call engineer for a shared AI platform. The job is not to click the most dramatic button. Read the evidence, identify the failure domain, choose the lowest-risk action, verify recovery and leave an auditable explanation.
Do not memorize product names first. Build the mental model, watch the system move, then map tools onto it.
Animated mental model
Operate the incident loop from signal to verified recovery
Strong operations turns symptoms into evidence, bounded change and post-change verification.
● Loading interaction engine…
● PAUSEDDetect user impact
01Signaluser + telemetryNOW
02Triagescope + timelineNOW
03Hypothesisfault domainNOW
04Mitigatebounded changeNOW
05Verifymulti-signalNOW
06Improvedurable controlNOW
STEP 1/6Detect user impact
Start with the actual symptom and affected population, not the component you expect to be broken.
p95 TTFT=8.9sLearning objectives
By the end of this module, you should be able to:
- Triage AI incidents from telemetry and identify the most likely failure domain.
- Choose mitigations that respect policy, evaluation gates and rollback safety.
- Verify recovery across performance, quality, security and cost signals.
- Produce an incident evidence trail and distinguish mitigation from permanent corrective action.
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Start with user impact and evidence, not your favorite component
Confirm the symptom, affected population and start time. Compare request traces, queue/capacity, provider health, release history, evaluation results and security signals.
Change one hypothesis at a time where possible. Broad speculative changes destroy evidence and increase blast radius.
- User impact and scope.
- Timeline/recent changes.
- Correlated metrics and representative traces.
- Quality/security/cost signals.
Common beginner misconception
The loudest dashboard is not always the root cause.
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Prefer reversible mitigation that reduces impact without violating hard constraints
Possible mitigations include shedding optional traffic, routing to an approved fallback, scaling available capacity, rolling back a bad release or disabling a risky tool path.
Do not bypass safety policy or send restricted data to an unapproved provider just to make latency green.
- Reversible and narrow change.
- Hard policy constraints preserved.
- Deadline/cost impact understood.
- Rollback/restore path known.
Common beginner misconception
Availability does not justify violating data/security constraints.
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Verify the original symptom and guard against secondary regressions
After a change, check the metric or user symptom that triggered the incident, but also inspect error rate, quality evaluation, security controls and cost. A mitigation can move the problem elsewhere.
Keep the incident open until sustained recovery meets predefined criteria.
- Original user/SLO symptom recovered.
- Quality gate still acceptable.
- No new security/policy violation.
- Cost/capacity sustainable after mitigation.
Common beginner misconception
One green graph is not enough evidence to close a complex AI incident.
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Close the loop: incident evidence should improve platform defaults
A post-incident review should identify contributing system conditions, not just the human who clicked a button. Convert repeated failure modes into tests, alerts, quotas, rollout gates, capacity policy or better platform defaults.
Distinguish immediate mitigation from corrective action. The on-call fix restores service; follow-up work reduces recurrence.
- Timeline and decision evidence.
- Contributing conditions and control gaps.
- Action owner and due date.
- Regression test/runbook/platform improvement.
Common beginner misconception
“Operator error” is rarely a sufficient root cause.
Interactive simulator
AI Platform Control Room
Run realistic synthetic incidents: queue saturation, provider outage, bad release, RAG regression, injection attempt, cost runaway and GPU scheduling failure.
● Loading simulator engine…
Control-room resultAwaiting decision
Inspect the evidence, choose a diagnosis and the safest bounded mitigation.
This simulator teaches relationships and diagnostic reasoning. Its synthetic values are not vendor benchmarks, hardware-sizing evidence or production SLO measurements.
Worked examples
Queue saturation incident
p95_ttft=9.2s ↑
queue_depth=110 ↑
decode_rate=normal
errors=0
recent_release=none
Hypothesis: capacity/queue pressure
Mitigation: shed optional traffic + add approved warm capacity
Verify: TTFT, queue, cost, quality- Evidence localizes delay before decode.
- Mitigation targets load/capacity rather than changing the model blindly.
Bad prompt release
latency=normal
errors=normal
task_success 0.91 -> 0.63
release prompt-v18 10 min ago
Mitigation: rollback prompt-v17
Verify: eval canary + user success- Infrastructure health is green.
- Evaluation and change history identify the semantic regression.
Try it · core lab · account-free
Run the incident-response capstone from the command line
Goal: Diagnose and mitigate seven synthetic production incidents, then verify recovery and produce an evidence summary.
Before you start
- Python 3 only. No cluster, GPU or external provider is required for the core capstone.
List incidents
python3 public/labs/llmops/16_production_ai_platform_capstone.py --listRun one guided incident
python3 public/labs/llmops/16_production_ai_platform_capstone.py --incident queue --guidedRun security and quality incidents
python3 public/labs/llmops/16_production_ai_platform_capstone.py --incident injection --guided
python3 public/labs/llmops/16_production_ai_platform_capstone.py --incident bad-release --guidedRun the full self-test
python3 public/labs/llmops/16_production_ai_platform_capstone.py --self-testVerify
- Seven incidents are available.
- Each incident has evidence, expected diagnosis, safe mitigation and verification criteria.
- Self-test reports PASS all_incident_paths.
Expected outcome
- You can move from symptom to fault domain using evidence.
- You can choose reversible mitigations without bypassing policy.
- You can explain what proves recovery and what follow-up prevents recurrence.
Break / fix · diagnose
- If your mitigation violates a hard policy, choose a degraded/fail-closed option instead.
- If the symptom improves but verification fails, keep the incident open and inspect secondary signals.
Production view
The capstone mirrors production incident reasoning: observe, localize, mitigate safely, verify, preserve evidence and improve the platform.
Production Engineer checklist
Before you call this concept production-ready:
This checklist is a study aid, not a proficiency score. Your evidence record remains based on tracked learning activity and later formal assessment.
Avoid these traps
Common mistakes
- Changing multiple unrelated controls before collecting evidence.
- Routing restricted data to an unapproved provider during an outage.
- Closing incidents when one metric improves without quality/security/cost verification.
- Stopping at mitigation without creating corrective actions or regression coverage.
Knowledge check
Reason about the system, not just the vocabulary.
Answer before opening each explanation. Opening all four answers records knowledge-check activity in My Learning.
1Queue depth and TTFT spike while decode rate stays normal. What is the strongest initial hypothesis?
Admission/capacity/queue pressure before generation, not slow token decoding.
2Latency is normal but task success collapses immediately after a prompt release. What action is strongly indicated?
Rollback or disable the bad prompt release, then verify with evaluation and user-impact signals.
3What makes an incident mitigation safer?
It is narrow, reversible, preserves hard policy constraints and has explicit verification/rollback criteria.
4What should a post-incident review produce?
Evidence-backed contributing conditions plus owned corrective actions such as new tests, gates, alerts, capacity controls or runbook improvements.
My Learning
Record your progress for this module.
Progress is saved in this browser immediately. When you sign in to My Learning, it can also sync across devices.
Attempt the core lab and knowledge checks before marking the module complete.