Individual skill
mle-workflow
Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.
pi/core/skills/mle-workflow/SKILL.md
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# Machine Learning Engineering Workflow
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.
## When to Activate
- 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
## Scope Calibration
