Many workflow engines exist nowadays, e.g. Argo Workflows, Tekton Pipelines, and Apache Airflow. However, their programming experience varies and they have different level of abstractions that are often obscure and complex. The code snippets below are some examples for constructing workflows using Apache Airflow and Kubeflow Pipelines.

Simplicity: Unified interface and imperative programming style for defining workflows with automatic construction of directed acyclic graph (DAG).
Extensibility: Extensible to support various workflow engines.
Reusability: Reusable steps for tasks such as distributed training of machine learning models.
Efficiency: Automatic workflow and resource optimizations under the hood.