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