MLOps and LLMOps

Map data, code, model, prompts and evaluation responsibilities.

Evidence: A lifecycle register names an owner for each artefact.

Record configurations, metrics and artefact hashes.

Evidence: A chosen experiment can be reproduced from its record.

Pin training or retrieval datasets and validate contracts.

Evidence: A change in schema triggers a controlled failure.

Register candidate versions with approval and dependency metadata.

Evidence: Each candidate points to immutable inputs and evaluation results.

Define critical checks and quality thresholds before release.

Evidence: A failing candidate cannot advance to the approved state.

Bundle runtime dependencies and check loading compatibility.

Evidence: A clean runtime loads the exact approved artefact.

Compare shadow, canary and full rollout approaches.

Evidence: A staged deployment limits exposure and records rollback thresholds.

Separate input shift, service faults and outcome degradation.

Evidence: Monitoring examples correctly identify each problem class.

Collect reviewed outcomes and handle delayed ground truth.

Evidence: Feedback records preserve provenance and access permissions.

Trigger a controlled update with regression checks.

Evidence: An update passes the same release criteria as the original.

Diagnose a degraded model or retrieval service and roll back.

Evidence: A timeline links symptom, diagnosis, action and recovery verification.

Assemble a versioned release record for technical review.

Evidence: A reviewer can trace production output to approved components.