Workflow management system
lastmod 2025-12-31created 2024-02-0113 edits
workflow management system (WfMS or WFMS) provides an infrastructure for the set-up, performance, and monitoring of a defined sequence of tasks arranged as a workflow application.
Projects
- NodeRed
- n8n
- Kubeflow
- Airflow
- JupyterFlow
- Argo Workflows
- jenkins
- Luigi
- MLflow
- Prefect
- DataJourney - Design-First open-source data management toolkit
- OpenWorkflow - Open-source workflow automation and execution platform that runs on Postgres alone
- Tersa - Visual AI workflow builder
Comparison
- [Recommended] Airflow vs Luigi vs Argo vs Kubeflow vs MLFlow
Airflow vs Kubeflow
| - | ||
| Form | DAG-based workflow | Pipeline-based workflow |
| Purpose | task(pipeline) orchestration | ML tasks such as ML training and experiment tracking |
| Features | Various tasks such as data pipelines, ML modeling, and infrastructure management | Specialized for ML tasks |
| Environment | Can run in various environments | Can run only in Kubernetes environments |
Airflow vs Argo workflow
| - | ||
| Category | Airflow | Argo workflow |
| UI | webserver | Argo UI |
| Scheduler | scheduler | kube-scheduler |
| Message queue | Celery | Argo Controller |
| Metadata DB | postgreSQL | etcd |
| Worker | Worker | Node |
| DAG definition | Python script | YAML |
| Job unit | Operator | Container |
Kubeflow vs MLFlow
Kubeflow and MLFlow are both smaller, more specialized tools than general task orchestration platforms such as Airflow or Luigi.
Kubeflow relies on Kubernetes, while MLFlow is a Python library that helps you add experiment tracking to your existing machine learning code. Kubeflow lets you build a full DAG where each step is a Kubernetes Pod, but MLFlow has built-in functionality to deploy your scikit-learn models to Amazon Sagemaker or Azure ML.
- Use Kubeflow if you want to track your machine learning experiments and deploy your solutions in a more customized way, backed by Kubernetes.
- Use MLFlow if you want a simpler approach to experiment tracking and want to deploy to managed platforms such as Amazon Sagemaker.