Individual skill

ml-pipeline

Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, builds feature store schemas with Feast, deploys model registries, and automates retraining and validation workflows. Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, managing experiment tracking systems, setting up DVC for data versioning, tuning hyperparameters, or configuring MLOps tooling like Kubeflow, Airflow, MLflow, or Prefect.

packages/codegraph/data/lifecycle-skills/ml-pipeline/SKILL.md

Source commit c19c0bc0a04d25a849ce749fe88250e688f06437

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Last checked

Nov 6, 2026

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