What DAG design patterns make an ML orchestration pipeline reliable in Airflow or Dagster?
Anyone can wire tasks into a DAG. What matters is the patterns that keep it correct under retries and backfills: idempotency, data-aware triggering, and the asset model. Here is what separates a flaky pipeline from a trustworthy one.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Anyone can wire tasks into a DAG. What matters is the patterns that keep it correct under retries and backfills: idempotency, data-aware triggering, and the asset model. Here is what separates a flaky pipeline from a trustworthy one.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
