// learn · devops
DevOps engineer to LLMOps
CI/CD, observability, and cost control are exactly what production LLM systems lack most. LLMOps is your discipline applied to non-deterministic, token-metered workloads. Here's where to start.
Start with the guide this path is built around: LLMOps for DevOps engineers.
The hard part of running LLMs in production is operations, not models — which is exactly what you already do.
What you know
CI/CD pipelines
What you'll build with it
Eval gates in CI — a labeled test set that blocks a deploy when answer quality regresses.
What you know
Metrics, logs, and tracing
What you'll build with it
LLM tracing — token usage, latency, and prompt/response capture across a single request.
What you know
Cost monitoring and budgets
What you'll build with it
Per-feature token budgets, caching, and model routing that keep spend predictable.
What you know
Containers and deployment
What you'll build with it
Model serving and rollout — versioning prompts and models like any other artifact.
Work these in order. Every link is free to read.
- 01Production-ready GenAI architecture
The layers that turn a demo into a system you can actually operate.
- 02Agentic AI
Understand agents and tools — the workloads you'll be asked to trace and scale.
- 03The AI Engineer Roadmap
The six-stage path from concept to offer, end to end.
- 04Interview prep
Prep the evals, cost, latency, and deployment questions LLMOps interviews focus on.
Production AI Notes
One practical AI engineering email each week
One concept, one architecture, one project idea, and one interview question — written for developers who want to build and ship real AI systems.