mle-workflow

Topics in the source

• Machine Learning Engineering Workflow
• When to Activate
• Scope Calibration
• Related Skills
• Reuse the SWE Surface
• Ten MLE Task Simulations
• Iteration Compact
• Decision Brain

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 Use only the lanes that fit the system in front of you. This skill is useful for ranking, search, recommendations, classifiers, forecasting, embeddings, LLM workflows, anomaly detection, and batch analytics, but it should not force one architecture onto all of them. Do not assume every model has supervised labels, online serving, a feature store, PyTorch, GPUs, human review, A/B tests, or real-time feedback. Do not add heavyweight MLOps

This is an excerpt. Refer to the complete source and its current requirements.