Airflow is a great choice for orchestrating complex workflows that involve batch processing and require dependency management. You would use Airflow when you need to schedule and monitor data pipelines or ETL jobs that are not necessarily tied to a machine learning workflow. For example, if you have nightly ETL processes that fetch data from APIs, transform it, and load it into a data warehouse, Airflow would be ideal.
Use Kubeflow when you are building and managing machine learning workflows, as it provides tools tailored for ML needs. A specific scenario would be when you need to perform hyperparameter tuning on model training and deploy the trained model in a Kubernetes environment.