Compare Airflow and Kubeflow for ML training pipelines
Tests orchestrator-to-workload fit. Strong answers contrast Airflow's data integration and Python DAGs with Kubeflow's K8s scaling, container reproducibility, and experiment tracking. Red flag: claiming one is always better without stage-specific reasoning.
Tests whether you can align orchestrator philosophy with end-to-end ML workload constraints. A strong answer contrasts Airflow's many data integrations and Python DAG flexibility with Kubeflow's Kubernetes-native orchestration, distributed training support, experiment tracking, and model serving. It should map data preprocessing to Airflow's ETL strengths, model training to Kubeflow's GPU scaling, and cite hybrid patterns. Red flag: treating them as interchangeable or recommending one without referencing specific pipeline stage requirements.
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