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How do you orchestrate ML pipelines (Airflow, Kubeflow, etc.), and what makes ML pipelines special?

ML workflows are multi-step DAGs (ingest, feature, train, eval, deploy), and orchestrators run them reliably. The signal is the DAG/scheduling model plus what's actually ML-specific: data deps, versioned artifacts, drift triggers, eval gates.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

ML workflows are multi-step DAGs (ingest, feature, train, eval, deploy), and orchestrators run them reliably. The signal is the DAG/scheduling model plus what's actually ML-specific: data deps, versioned artifacts, drift triggers, eval gates.

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