Checklist

Predictive Model Validation & Deployment Checklist

An interactive, auditable pre-deployment checklist to verify model validity, fairness, performance, operational readiness, and rollback plans before releasing predictive models to production.

Interactive Tool

Predictive Model Validation & Deployment Checklist

Use this checklist to verify readiness before releasing a predictive model to production. Mark each item, add evidence or links, assign an owner, and capture a final sign‑off. This record can be used for audits, post‑mortems, and to trigger deployment when conditions are met.

Name or identifier for the model and artifact (repository tag, artifact id).
Version, commit hash, or artifact id.
Person or team responsible for deployment and post-deploy monitoring.
YYYY-MM-DD or sprint/iteration.
Estimate before deployment.
1 = Not ready, 5 = Ready to deploy
1.0 10.0
Have you ruled out target leakage (temporal or feature leakage) between training and test sets?
Link to tests, notebooks, or results (describe test method).
Temporal backtest, out-of-time holdout, or cross-validation appropriate to the use case performed?
Describe datasets, time windows, performance on holdout, and relevant metrics (AUC, RMSE, lift, etc.).
For probabilistic outputs, has calibration been evaluated and corrected if needed?
Provide calibration plots, Brier score, isotonic/logistic calibration steps or notes.
Bias scans for protected groups and subgroup performance checks completed?
Metrics, subgroup performance, chosen mitigations, and link to fairness report.
Are feature sources, transformations and upstream changes documented and is drift analysis performed?
Data lineage links, feature store references, and expected change frequency.
Are production metrics (input data, model outputs, model performance, and business KPIs) instrumented with alert thresholds?
Where are dashboards, alert thresholds, and escalation plans documented?
Has the model been load-tested and validated against expected SLOs (latency, throughput, resource usage)?
Latency p95, throughput targets, resource profile, and test scripts or reports.
Is there a plan for shadowing, canarying, or staged rollout and a clear metric set for judging canary health?
Canary traffic percentages, duration, metrics to watch, and rollback windows.
Are explicit rollback triggers defined and are automated or manual rollback procedures tested?
Metric thresholds, who performs rollback, and runbook links.
Are triggers for retraining defined, and are pipelines available to retrain and validate new models?
Retrain frequency, data windows, validation required for a new model, and model promotion criteria.
Any required privacy impact assessment, security review, or regulatory checks performed and documented?
Links to reviews, approvals, or exceptions.
List unresolved issues or residual risks to monitor after deployment.
Name and role of person authorizing deployment.
Anything else to record (mitigations, stakeholders to notify, related change requests).
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