Checklist

MLOps Production Readiness Checklist

Interactive, actionable checklist for moving ML models from prototype to production. Each checklist item includes a readiness yes/no, owner, and notes/acceptance criteria so teams can capture decisions, assign responsibility, and save the result to organizational memory.

Interactive Tool

MLOps Production Readiness Checklist

Use this checklist to reduce surprises when promoting a model to production. For each topic, mark readiness, assign an owner, and capture acceptance criteria or next steps. Save the checklist to keep a record that supports post-deployment review and continuous improvement.

Suggested use: run this as part of your release gate. If any item is marked 'No', attach a remediation plan and a target date before deployment.

Are model artifacts, training code, data snapshots, and environment captured in a versioned registry or repository?
Person or team responsible for model artifacts and reproducibility.
E.g., artifact stored with immutable ID, training dataset snapshot available, pipeline reproducible from code and config.
Are input data validation checks, schema enforcement, and anomaly detection in place for production inputs?
Team responsible for data quality and validation pipelines.
E.g., validators for nulls/types/outliers, schema evolution policy, fallback behavior defined.
Are monitoring metrics defined (data drift, concept drift, input quality, prediction distributions) and alerts configured?
Team responsible for monitoring and response.
E.g., metrics thresholds, alerting channels, dashboards, and runbook links.
Is the rollout plan chosen and automated (traffic splitting, target cohorts, feature flags)?
Team responsible for deployment orchestration and rollout.
E.g., canary target sizes, metrics to watch during rollout, rollback criteria.
Are automated rollback triggers and incident response playbooks defined and tested?
Person/team that executes rollbacks and leads incident response.
E.g., tested rollback steps, communication plan, post-incident RCA owners.
Are resource budgets, autoscaling limits, and cost alerts configured to prevent runaway spend?
Team tracking cloud spend and resource controls.
E.g., guardrails for batch jobs, concurrency limits, expected per-request cost estimates.
Is there a defined retraining trigger, data pipeline, and testing workflow for model updates?
Team responsible for maintaining retraining pipelines and model lifecycle.
E.g., drift threshold that triggers retrain, validation gating, canary for new model.
Are logs, traces, and standardized metrics available for debugging and capacity planning?
Team that maintains logs, APM, and traces.
E.g., structured logs, correlation IDs, SLOs/SLA definitions, retention policies.
Are access controls, secret management, and data privacy safeguards implemented?
Security or platform team contact for approvals and audits.
E.g., least privilege, encrypted storage, compliance checklist items.
Has the model been load-tested under expected and peak scenarios, with acceptable latency and resource use?
Team responsible for performance testing and capacity planning.
E.g., test results, SLO targets, degraded-mode behavior documented.
Are runbooks, owner lists, expected outputs, and troubleshooting steps documented?
Person responsible for keeping docs and runbooks current.
E.g., links to runbooks, post-deploy checklist, SLA contact information.
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