Patterns and metrics to detect model drift, diagnose performance regression, and design safe, auditable retraining triggers and runbooks.
A practical playbook to keep models reliable and trustworthy: defines meaningful health metrics, shows how to detect data and concept drift, describes tiered alerting and triage, and gives safe, auditable retraining workflows (including canary testing, validation gates, and owner responsibilities).
A practical, fillable runbook for deploying, operating, observing, validating, governing, and retraining production ML models. Includes deployment gates, canary and rollout plans, concrete rollback criteria, monitoring KPIs (with example thresholds), input drift and bias checks, retraining triggers and schedules, security and data handling controls, and incident response and post-incident review steps.
A practical, actionable specification for a model-monitoring dashboard that makes model health visible and actionable for engineers, data teams, and business stakeholders. Includes recommended KPIs with calculation guidance, distribution and calibration checks, drift detection methods, alert thresholds and prioritization, drillpaths for root-cause analysis, required data inputs, roles and runbooks for incidents, and safe retraining and auditability practices.
An operational runbook that defines practical health signals, alerting and prioritization, step-by-step triage, temporary mitigation and rollback procedures, a root-cause investigation template, post-incident governance steps, and a safe, auditable retraining playbook with human-in-the-loop checks.
A practical, operational dashboard template for monitoring model health: combines data and concept-drift indicators, prediction distributions, business KPI decay tracking, feature-level drift diagnostics, data quality signals, retraining triggers, model versioning, and example alert rules with investigation runbook steps.