Orchestration tools for ML pipelines — Airflow vs Prefect vs Dagster.

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Answer

$Airflow: mature, ubiquitous, DAG-based scheduler; Python operators.$
$Downside: verbose, poor local dev experience, weak lineage.$
$Prefect: modern Airflow alternative; better local dev + retries; dynamic DAGs.$
$Dagster: asset-based (materialization view, not just task view); best lineage + observability; steeper learning curve.$
$Kubeflow Pipelines: Kubernetes-native.$
$For ML: Dagster's asset model matches ML thinking best.$
$Use Airflow when org already has it.$

Check yourself — multiple choice

Random
Airflow: mature + verbose; Prefect: better dev UX + dynamic; Dagster: asset-based + best lineage; Kubeflow: K8s-native; Dagster's asset model matches ML best
All same
Not real

Orchestration: Airflow / Prefect / Dagster / Kubeflow; Dagster asset best for ML.

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