• Apache Airflow if you want the most full-featured, mature tool and you can dedicate time to learning how it works, setting it up, and maintaining it.
• Luigi if you need something with an easier learning curve than Airflow. It has fewer features, but it’s easier to get off the ground.
• Argo if you’re already deeply invested in the Kubernetes ecosystem and want to manage all of your tasks as pods, defining them in YAML instead of Python.
• KubeFlow if you want to use Kubernetes but still define your tasks with Python instead of YAML.
• MLFlow if you care more about tracking experiments or tracking and deploying models using MLFlow’s predefined patterns than about finding a tool that can adapt to your existing custom workflows.”

Artigo completo (vale muito a pena a leitura):

https://towardsdatascience.com/airflow-vs-luigi-vs-argo-vs-mlflow-vs-kubeflow-b3785dd1ed0c