Checklist

Model Deployment & Production Checklist

A practical, interactive pre-deployment and production checklist that captures owner sign-off, evidence, and readiness for safe model rollout. Includes explicit checks for validation, monitoring, rollback readiness, data contracts, and operational responsibilities — and lets teams save submissions for audit and follow-up.

Interactive Tool

Model Deployment & Production Checklist

This checklist helps teams move a trained model into production with standard safeguards. Use the form to record who is responsible, the target environment, short answers for each required check, and links to supporting evidence (tests, reports, runbooks). Saving the checklist stores a timestamped submission that can be reviewed later for audits or continuous improvement.

Instructions: for each item, choose Yes if the check is completed and evidence is attached or linked. Use the notes field to add context or references to tickets, reports, dashboards, or pull requests.

Person accountable for this deployment (name and role).
Email, Slack handle, or change/issue ticket ID for follow-up.
Where the model will run
Local date/time or CI pipeline run identifier.
Has the business owner signed off and are acceptance criteria documented?
Are test datasets, performance baselines, and regression test results attached or referenced?
Have fairness, bias, and explainability checks been performed and documented?
Are production metrics defined (accuracy, latency, throughput, data quality) and alerts configured?
Are input/output schemas, versions, and validation checks in place and agreed with producers/consumers?
Is there a staged rollout or canarying plan with traffic splits, metrics to watch, and success criteria?
Are retraining triggers (data drift, performance drop) defined and is there an operational retraining plan?
Are logs, traces, feature-level metrics, and request/response sampling configured for troubleshooting?
Is there a tested rollback procedure, runbook, and assigned on-call contacts?
Are deployment docs, runbooks, model cards, and responsible teams documented and handed off to operations/support?
Briefly list known model limitations, data-sensitivity concerns, regulatory notes, or open risks.
Provide links to artifacts that justify Yes answers (test results, monitoring dashboards, PRs, runbook IDs, audit records).
Anything else the reviewer should know.
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