Applied LLMOps
P11.applied-llm-systems.03 · Audience: guest, it-ml, language-pro · Prerequisites: Re-ranking & IR metrics, Agentic systems, tools & MCP
Operating a patient-facing search + assistant in production: frame it before you draw it, allocate the end-to-end SLA across the answer path, degrade gracefully when the budget is blown, and add ML-specific observability on top of service metrics. (Cross-ref: P13 Production ML & MLOps.)
Before any architecture, state the non-functional frame: QPS, p95/p99 latency targets, freshness, availability SLA and what “degraded” means (e.g. 99.9%; lexical-only if the LLM is down). An interviewer wants the numbers first — they decide every downstream choice.
Ask the mentor about this module
Ask a question about this content. The mentor explains and grounds its answer in what you are studying; asking is recorded as a learning signal, not a grade.
🎓 Practice ladder
2 graded rungs · ~20 minNow build the LLMOps maths yourself. Each rung is a three-panel workspace: instructions on the left, a code editor in the middle, output + test results on the right. Run checks the visible tests; Submit grades against hidden edge cases — a total exactly at the SLA still passes, the dominant stage breaks ties by order, and the batcher flushes on size or deadline with per-request wait math. The senior rung sizes the request-stream batches.
Rung 1 — Latency budget verdict
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Senior rung — Batch the request stream
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⚡ Interview Ref — the quick-scan Reference face
- Frame first: QPS, p95/p99, freshness, availability SLA + what “degraded” means. Numbers before architecture.
- Architecture: retrieve → re-rank → generate → guardrails, behind cache + router; design the degradation path (LLM down → lexical-only) up front.
- Serving: self-host (control, in-region, ops burden) · vendor API (fast, new processor / cross-border) · router (cheapest model that clears quality).
- Latency budget: allocate the SLA per stage → total, headroom, dominant stage; shed the dominant stage when blown.
- Observability: ML-specific (recall/groundedness, quality, abstain rate, drift, cost/answer) on top of service metrics.
- Model platform: versioned models + indexes, blue/green, eval gates, feedback loop.
Try it yourself
A scratch console for this page's ideas — ungraded, nothing you run here is recorded.
Scratch console
A scratch console with the scientific stack (pandas, numpy, scikit-learn). Runs on the server — no network, resource-limited and measured.
Output appears here.
Where next?
Later in Applied LLM Systems & Agents