Ml Pipeline Automation

This gets you up and running with Airflow, Kubeflow, and MLflow for orchestrating ML workflows from data ingestion to deployment. The quick start puts a working pipeline with experiment tracking in your hands in five minutes, which is honestly the right way to teach this stuff. You get practical patterns for the usual pain points: task failures that go unnoticed, XCom data mysteriously disappearing between tasks, DAGs that won't show up in the UI. The comparison table between orchestration tools is genuinely useful if you're still figuring out whether you need Airflow's flexibility or Kubeflow's Kubernetes integration. Real talk, most of the value here is in the error prevention examples because that's where teams actually lose time.

skills.sh installs: 402
GitHub stars: 248
last updated: Aug 21, 2026

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Install

$ npx -y skills add secondsky/claude-skills --skill ml-pipeline-automation --agent claude-code

Installs into .claude/skills of the current project.

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ML Pipeline Automation

Orchestrate end-to-end machine learning workflows from data ingestion to production deployment with production-tested Airflow, Kubeflow, and MLflow patterns.

When to Use This Skill

Load this skill when:

Building ML Pipelines: Orchestrating data → train → deploy workflows
Scheduling Retraining: Setting up automated model retraining schedules
Experiment Tracking: Tracking experiments, parameters, metrics across runs
MLOps Implementation: Building reproducible, monitored ML infrastructure
Workflow Orchestration: Managing complex multi-step ML workflows
Model Registry: Managing model versions and deployment lifecycle

Quick Start: ML Pipeline in 5 Steps

# 1. Install Airflow and MLflow (check for latest versions at time of use)
pip install apache-airflow==3.1.5 mlflow==3.7.0

# Note: These versions are current as of December 2025
# Check PyPI for latest stable releases: https://pypi.org/project/apache-airflow/

# 2. Initialize Airflow database
airflow db init

SKILL.md on GitHub