LLMOps is the engineering work required to run language-model applications reliably after the demo works: serving models, controlling access, observing requests, measuring quality, managing cost, releasing changes and recovering when something fails.
Engineering focus: LLMOps, MLOps and AI platform responsibilities
LLMOps is the engineering work required to run language-model applications reliably after the demo works: serving models, controlling access, observing requests, measuring quality, managing cost, releasing changes and recovering when something fails.
Do not memorize product names first. Build the mental model, watch the system move, then map tools onto it.
Play the animation slowly. Each added box exists because a new operational problem appeared.
One application calls one model. Useful for learning, but there is no operational safety net yet.
On a laptop, success may mean that one prompt returns one useful answer. In production, success is wider: many users must receive acceptable answers within expected latency, failures must be visible, access must be controlled, costs must stay bounded and changes must be reversible.
LLMOps begins where the happy-path demo stops. It asks what must exist around the model so an organization can depend on the application.
LLMOps is not simply 'install vLLM' or 'put a model in Kubernetes'. Those may be implementation choices; LLMOps is the operational discipline around the full AI service.
DevOps provides reusable practices for building, releasing and operating software. MLOps adds the lifecycle of trained models and data. LLMOps focuses on the production concerns of foundation-model applications such as inference serving, prompts, context, retrieval, evaluation, token economics and model/provider routing.
AI platform engineering packages these capabilities into reusable internal services so every application team does not rebuild authentication, model access, telemetry, evaluation and policy from scratch.
LLMOps does not replace DevOps or SRE. It builds on them.
Operating an AI system is a feedback loop. The service handles requests. Telemetry records what happened. Evaluation decides whether behavior remains acceptable. Changes are then tested and released deliberately.
Without this loop, teams are reduced to anecdotes: someone says the model feels slower, smarter or worse, but nobody has evidence.
The important thing is to understand the boundary and evidence before memorizing implementation details.
If every product team directly integrates a different model provider, invents its own logging and writes its own retry logic, the organization accumulates inconsistent security and operating behavior.
An AI platform centralizes the capabilities that should be common while allowing application teams to keep ownership of domain-specific prompts, retrieval logic and user experience.
A platform is not just a portal. It is a product of reusable contracts, automation and operating capabilities.
PROTOTYPE
User → Python script → local model
PRODUCTION
User → application → auth → gateway → inference backend
↘ telemetry / policy / cost / evalChange: model-v4 → model-v5
Ask:
1. Did latency change?
2. Did task success change?
3. Did cost change?
4. Did safety regress?
5. Can we roll back?Goal: Build the habit of asking what operational capability is missing instead of jumping straight to a product name.
python3 public/labs/llmops/01_map_llmops_responsibilities.pyChoose the capability that should address the problem: serving, routing, reliability, observability, evaluation, security/governance or cost/capacity.
After each answer, compare the reason—not just the label.
Describe a failure that could happen between a user and a model, then identify the capability that would detect or control it.
During incidents, strong operators reason from symptoms to responsibilities and evidence before choosing a tool or changing production.
This checklist is a study aid, not a proficiency score. Your evidence record remains based on tracked learning activity and later formal assessment.
Answer before opening each explanation. Opening all four answers records knowledge-check activity in My Learning.
The problem moved from model capability to service operation. LLMOps and platform controls are now required around the model.
It reuses software delivery and reliability practices while adding AI-specific concerns such as inference, model routing, context, evaluation and token economics.
Quality evaluation. Infrastructure telemetry cannot prove semantic task success.
Provide reusable, governed capabilities such as model access, routing, telemetry and evaluation so application teams do not rebuild them independently.
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Attempt the core lab and knowledge checks before marking the module complete.