Checklist

Deployment & MLOps Readiness Checklist

Interactive checklist to evaluate prototype readiness for lightweight MLOps, monitoring, and maintainability. Collects verifiable status, ownership, evidence, confidence, and improvement notes so teams can track readiness and preserve learning.

Interactive Tool

Deployment & MLOps Readiness Checklist

Use this checklist to rapidly assess whether a machine-learning prototype is ready to operate reliably with lightweight MLOps practices. The goal is to preserve the learning loop while adding the minimum reproducible controls for observation, ownership, rollback, cost and data safety. For each item record the status, owner, evidence, confidence (1–5), and short notes describing any follow-up actions.

Short name that identifies the prototype (used for dashboards, registries, and records).
Enter date (YYYY-MM-DD) or 'today'.
Person or team completing this checklist.
Can the model be rebuilt from saved code, data references, and a reproducible training script?
Links to scripts, container images, dataset manifests, or brief reproduction steps.
Who is accountable for keeping reproducibility records up to date.
1 = very low confidence, 5 = high confidence that rebuild will succeed.
1.0 10.0
Are model artifacts, code and data references versioned and recorded in a registry or artifact store?
Registry links, commit IDs, artifact URIs, changelog notes.
Is training automated enough to re-run on updated data/config with minimal manual steps?
Are tests and CI configured to validate model code, infra as code, and packaging before deployment?
Links to CI pipeline, test coverage summary, or example build logs.
Have you measured latency under expected load and validated stability across common inputs?
Test results, load test reports, SLO definitions.
Is drift detection in place (data distributions, feature statistics, label drift) and are alerts configured?
Dashboard links, alert rules, sample alerts.
Are there documented rollback steps, canary deployments, or feature flags to limit impact?
Runbook links, deployment playbooks, test rollbacks performed.
Are expected runtime and storage costs estimated and are cost limits/alerts configured?
Estimated monthly cost, budget owner, alerts or quotas set.
Are data access, retention, anonymization, and PII safeguards documented and enforced?
Policy links, access control lists, anonymization steps.
Is there an incident runbook describing monitoring thresholds, responsibilities, and rollback steps?
URL or path to incident runbook.
Is there a designated owner for runtime operations and an agreed SLA (uptime, latency, support)?
Name of owner, contact method, and summary of SLA or expectations.
Summary of the most important actions required to reach intended graduation or to mitigate immediate risks.
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