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.