Many teams use Airflow to manage multi-stage workflows, but when scaling from local to production, it relies on Celery or Kubernetes, which can be difficult and time-consuming to set up. Modal is a simpler way to manage GPUs and containerized environments, making it ideal for AI/ML workflows. Modal can be triggered directly from an Airflow DAG and serves as a replacement for the Celery or Kubernetes executor. The process involves installing Modal in the Airflow environment, setting token IDs and secrets, and using either the `lookup` function to deploy functions or creating a custom operator that uses Modal Sandboxes to run Python code. This allows teams to isolate their task environment from their Airflow environment, making it easier to manage GPUs and containerized environments.