Platform / SRE

LLMOps & MLOps

What changes operationally when the artifact is a model, and what does not.

Who this is for

Platform, DevOps, and SRE engineers running AI workloads on a rota.

What you should be able to do

Operate a model in production with drift detection, evaluation gates, and a retraining path.

The delivery flow

The order the work actually happens in. Each step is where a decision gets made and written down, not a chapter heading.

CI + eval gate
Model registry
Deployment strategy
Serving + autoscale
Telemetry
Drift detection
Retraining trigger
Rollback

Reading path

9 published guides on this track. Every one is a live page on this site — nothing here is a placeholder.

Reference implementations

Citadel’s open-source repositories for this track — Terraform modules, MCP servers, and reference architectures you can read, fork, and run. Apache/MIT licensed; check each repository for its terms.

Primary sources

The standards, framework documents, and vendor references this track is built against. Go here when you need the authoritative wording rather than a summary — in a security review or an audit, the source is what counts.

Other tracks

Talk through this with an architect