Operational recipes and templates for building continuous validation pipelines, staged canaries, drift detection, and safe retraining cycles for production ML.
A practical, operational runbook with deployment patterns, observability and alerting guidance, drift-detection recipes, retraining triggers and safe retraining procedures, versioning and rollback practices, cost-control ideas, incident response templates, and SRE handoff guidance to keep production models reliable and maintainable.
A practical, ready-to-adapt template for continuous validation: test catalog, staged canary rollout steps, essential metrics and thresholds, automated sampling rules, drift detection settings, retraining patterns, and explicit rollback and approval criteria to keep production models safe and effective.
A practical, operational playbook that turns the five high-level steps of continuous validation into concrete recipes: what to measure, how to detect drift, patterns for safe canary rollouts, when and how to retrain, and disciplined rollback and postmortem practices.
A practical, operational playbook describing pipeline architecture, sampling and signal strategies, canary and A/B evaluation patterns, automated retrain triggers, rollback rules, and human escalation paths for keeping models safe and useful in production.