Airflow vs Kubeflow: What are the differences?
Airflow: A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb. Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed; Kubeflow: Machine Learning Toolkit for Kubernetes. The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
Airflow and Kubeflow are primarily classified as "Workflow Manager" and "Machine Learning" tools respectively.
Airflow and Kubeflow are both open source tools. It seems that Airflow with 13.3K GitHub stars and 4.91K forks on GitHub has more adoption than Kubeflow with 7.23K GitHub stars and 1.08K GitHub forks.
Airbnb, Slack, and 9GAG are some of the popular companies that use Airflow, whereas Kubeflow is used by Eliiza, Hepsiburada, and Big Insight. Airflow has a broader approval, being mentioned in 98 company stacks & 162 developers stacks; compared to Kubeflow, which is listed in 3 company stacks and 8 developer stacks.