Practical guidance and reusable templates for deploying, observing, validating, governing, retraining, and scaling ML models in production.
Concrete, actionable runbook for model deployment, canarying, rollback, monitoring, alert thresholds, and incident triage. Includes pre-deployment checks, step-by-step deployment and rollback procedures, incident impact assessment and containment guidance, post-incident RCA template, and ready-to-use stakeholder communication templates.
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, step-by-step playbook for packaging, testing, deploying, observing, validating, governing, and rolling back machine learning models in production. Includes checklist sections, example CI/CD and deployment YAML snippets, monitoring metrics, drift detection patterns, retrain triggers, alerting runbooks, and a minimal SLA template for models.
A practical, step-by-step runbook for safe model rollouts: pre-deployment checks, a concrete canary plan with traffic slices and evaluation windows, monitoring widgets and alert thresholds, precise rollback criteria and procedures, post-deployment validation, retraining triggers, and ready-to-send communication templates for incidents.
A practical, step-by-step playbook that walks teams from use-case framing to reliable production: prepare data, build defensible baselines, validate with time-aware protocols, choose safe deployment patterns, instrument monitoring and alerts, and govern human-in-the-loop controls. Includes templates, acceptance criteria, common failure modes, and handoff checklist.
A practical, reusable suite: pre-validation checklist, unit test templates for inputs/outputs, adversarial and edge-case tests, fairness checks, threshold calibration guidance, performance/regression test templates, and a ready-to-use model validation report template for governance and approval.
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).