Deployment & Human-in-the-Loop Checklist

Deploy predictions only when there's a clear plan for how humans will see, trust, act on, and override them. This checklist focuses on safety, clarity, and observability for a pilot deployment.

Pre-deployment

• Document acceptance criteria (minimum precision/recall, false-alarm rate) and who signs off.
• Prepare a baseline run showing current process outcomes to compare against.
• Build simple explainability artifacts for each prediction (top 3 drivers, recent examples).
• Create a runbook describing typical alerts, triage steps, and expected actions.

Staging and rollout

• Stage in a non-production environment and run a shadow mode that logs predictions without affecting decisions.
• Run a canary or pilot with a small subset of users or geography before broad rollout.
• Provide training for decision owners and a feedback channel for bad predictions.

Human-in-the-loop controls

• Expose model confidence and short explanation on every prediction.
• Allow decision owners to flag or correct labels; capture this feedback for retraining.
• Define manual override rules and record overrides for auditing.

Alerting, escalation and SLAs

• Define alert thresholds and who will be notified (include on-call rotations if applicable).
• Set service-level objectives for prediction delivery latency and error-handling.

Versioning, rollback, and retraining

• Implement model and feature-store versioning linked to deployment artifacts.
• Define rollback criteria (e.g., sudden metric drop, unexpected behavior) and a tested rollback process.
• Choose a retraining cadence or data-driven retrain triggers (performance drops, data drift, calendar intervals).

Post-deployment review

• Hold a post-launch review after a defined pilot period to evaluate impact, false-alarm fatigue, and adoption.
• If the model will affect safety or regulatory decisions, schedule formal audits and document decisions and actions taken.