ML System Lifecycle
The ML system lifecycle is the controlled path from problem framing to data creation, training, evaluation, deployment, monitoring, incident response, retraining, and retirement. It is broader than model development because production behavior depends on data, code, infrastructure, policies, and humans.
Stages and their artifacts
Each stage should produce an artifact that the next stage consumes: a decision brief, dataset manifest, training run, evaluation report, model version, serving contract, rollout record, monitoring dashboard, and retirement note. The lifecycle is circular only when monitoring or incidents create new evidence that justifies another training or policy cycle.
flowchart TD Frame[Frame: problem and harm model] --> BuildData[Build data: dataset manifest] BuildData --> Train[Train: tracked run] Train --> Evaluate[Evaluate: slice gates] Evaluate --> Deploy[Deploy: model version and serving contract] Deploy --> Operate[Operate: monitoring and incident response] Operate --> Retire[Retire when value ends] Operate --> Train
Artifact: Lifecycle Gates
lifecycle:
frame:
artifact: decision_brief.md
gate: owner and harm model approved
build_data:
artifact: dataset_manifest.yaml
gate: schema and label policy reviewed
train:
artifact: tracked_run
gate: reproducible run in experiment tracker
evaluate:
artifact: release_eval_report.html
gate: all slice gates pass
deploy:
artifact: model_version + serving_contract
gate: rollback target and SLOs defined
operate:
artifact: monitoring + incident runbook
gate: alerts routed to an ownerTraining pipelines, ci-cd-for-ml, and model-versioning automate pieces of this lifecycle, but they do not replace ownership decisions.
Failure Modes
Lifecycle diagrams fail when no gate can block release. Another failure is indefinite operation: models need retirement criteria when the product changes, labels disappear, or the maintenance cost exceeds value.
References
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Section — ML Engineering and MLOps
Learning path — Production ML