ml-pipeline
适合需要稳定模型上线与持续训练的工程团队。
设计并实现生产级 ML 流水线、训练编排、模型注册与自动化重训练。
设计并落地生产级 ML 流水线，从数据校验、特征存储到训练编排、模型注册和自动化重训练全链条覆盖。常用工具包括 MLflow、Weights & Biases、Kubeflow/ Airflow、Feast、DVC 等，适合需要可重复、可监控的模型部署与持续训练场景。重点是把实验与运维工作自动化，保证模型生命周期的可靠性与可审计性。
▸ 展开 SKILL.md 英文原文
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.
帮我安装这个 skill：https://raw.githubusercontent.com/Jeffallan/claude-skills/main/skills/ml-pipeline/SKILL.mdcurl -fsSL "https://raw.githubusercontent.com/Jeffallan/claude-skills/main/skills/ml-pipeline/SKILL.md"# ML Pipeline Expert Senior ML pipeline engineer specializing in production-grade machine learning infrastructure, orchestration systems, and automated training workflows. ## Core Workflow 1. **Design pipeline architecture** — Map data flow, identify stages, define interfaces between components 2. **Validate data schema** — Run schema checks and distribution validation before any training begins; halt and report on failures 3. **Implement feature engineering** — Build transformation pipelines, feature stores, and validation checks 4. **Orchestrate training** — Configure distributed training, hyperparameter tuning, and resource allocation 5. **Track experiments** — Log metrics, parameters,