ml-pipeline

Name: ml-pipeline
Author: Jeffallan

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.

pipeline ml

Content Preview

---
name: ml-pipeline
description: "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 s

How to Use

Recommended: Install to project (local)

mkdir -p .claude/skills
curl -o .claude/skills/ml-pipeline.md \
  https://raw.githubusercontent.com/Jeffallan/claude-skills/main/skills/ml-pipeline/SKILL.md

Skill is scoped to this project only. Add .claude/skills/ to your .gitignoreif you don't want to commit it.

Alternative: Clone full repo

git clone https://github.com/Jeffallan/claude-skills

Then reference at skills/ml-pipeline/SKILL.md