MLOps Retraining Loop: safe automation with guardrails

Why this is worth your time

• Offline gains can still degrade live KPIs due to skew and traffic shift.
• Without dataset/feature lineage, incident RCA becomes guesswork.
• Promotion without canary + rollback turns retraining into an outage generator.

Architecture pattern

• Data/versioning: DVC or LakeFS for dataset + feature snapshots tied to each model build.
• Orchestration: Kubeflow/Argo pipelines with explicit stages (extract → validate → train → eval → package).
• Registry: MLflow/SageMaker Registry controlling promotion (dev → staging → prod).
• Observability: drift + performance slices exported as metrics and alerted on trends.

Sharp edges

• Feature skew (training vs serving) is the silent killer — validate parity continuously.
• Don’t auto-promote purely on offline metrics; use shadow/canary evaluation.
• Rollback must be a first-class API (model version pin + fast config flip).

Production checklist

• Every model build is tied to: code SHA, dataset version, feature schema version.
• Shadow traffic or canary evaluation exists before full rollout.
• Rollback path is tested (not documented).
• Monitoring includes drift + business KPI slices (tenant/region/version).

Copy/paste snippets

# p95 prediction error over 30m (example)
histogram_quantile(0.95, sum(rate(prediction_error_bucket[30m])) by (le))

# breach for 15m
(predict_error_p95 > 0.20)
AND on() (count_over_time(predict_error_p95[15m]) > 0)