MLOps Pipeline Review Checklist

When to Use This Checklist

Use this checklist to audit the maturity of an end-to-end machine learning operations (MLOps) pipeline. MLOps applies software engineering discipline to the machine learning lifecycle, covering data, training, evaluation, deployment, and monitoring. This review identifies gaps in reproducibility, automation, and governance before they cause unreliable models in production.

How to Use This Checklist

Begin by mapping the whole pipeline so you understand every stage and handoff. Then verify the foundations: versioned data, tracked lineage, and reproducible training runs. Move outward to the registry, evaluation gates, and CI/CD that enforce quality automatically. Finish with monitoring, security, cost, and incident runbooks. Score each area as a maturity check rather than a simple pass/fail, and record the highest-impact gaps for remediation.

What Good Looks Like

A mature pipeline rebuilds any model from pinned code, data, and environment. A model registry records versions, stages, and approvals, and automated evaluation gates prevent weak models from reaching production. CI/CD tests pipeline code, not just application code. Monitoring detects data and prediction drift and alerts owners. Access to datasets and secrets follows least privilege, costs are tracked, and runbooks cover rollback and incidents.

Common Pitfalls

Many pipelines cannot reproduce a past model because data was not versioned. Manual promotion without evaluation gates lets quality slip. Drift monitoring is often absent, so degradation goes unnoticed for weeks. Training/serving skew creeps in without a shared feature definition. Finally, broad dataset access and unmanaged secrets create avoidable security exposure.

Related Resources

Review MLOps principles, model monitoring and drift detection, data version control, feature store practices, and data lineage guidance.