/ml-pipeline
設計並實作正式環境等級的 ML pipeline：用 MLflow 或 Weights & Biases 追蹤實驗、建立 Kubeflow 或 Airflow DAG、用 Feast 建 feature store、部署 model registry、自動化重新訓練與驗證。
- 安裝次數
- 4000
- 專案星數
- 1.2 萬 ★
- 授權
- MIT
- 專案更新
- 2026-10-30
怎麼裝
npx skills add https://github.com/Jeffallan/claude-skills --skill ml-pipeline安裝前請先看過原文內容。中文說明是 AI 翻譯，不保證安全。
原文
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.