Use this skill to turn model work into a production ML system with clear data contracts, repeatable training, measurable quality gates, deployable artifacts, and operational monitoring. - Planning or reviewing a production ML feature, model refresh, ranking system, recommender, classifier, embedding workflow, or forecasting pipeline - Converting notebook code into a reusable training, evaluation, batch inference, or online inference pipeline - Designing model promotion criteria, offline/online evals, experiment tracking, or rollback paths - Debugging failures caused by data drift, label leakage, stale features, artifact mismatch, or inconsistent training and serving logic - Adding model monitoring, canary rollout, shadow traffic, or post-deploy quality checks