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Workflow Orchestrating

Airflow​

  • General-purpose workflow orchestration for data engineers, DevOps, backend developers.
  • Goal: task scheduling with time-based or DAG dependencies.
  • Orchestrator: Python-based DAG engine
  • Deployment: Anywhere, local, VM, Docker, K8s
  • Execution: Operators run as Python code, potentially in Docker or K8s
  • Scalability: Scales via Celery/KubernetesExecutor
  • Storage: External storage (e.g., S3, GCS, databases)
  • ML Pipeline: DAGs in pure Python
  • UI: DAG UI with task-level monitoring
  • Artifacts: Not built-in; needs plugins
  • Hyperparameter tuning: Requires external integration
  • Serving models: Not built-in
  • Experiment tracking: Requires MLflow or similar integrations
  • Security: RBAC via Airflow UI or external auth
  • Plugins: Airflow operators, hooks, sensors
  • Cloud: First-class support for all cloud providers

Best for​

ETL and batch data pipelines, data warehousing and transformation, scheduled workflows, general task automations.

ML-centris pipelines requires manual integration

Kubeflow​

  • ML workflow orchestration for data scientists and ML engineers.
  • Goal: scalable machine learning pipelines and model lifecycle on Kubernetes.
  • Orchestrator: Argo Workflows (Kubernetes-native engine)
  • Deployment: Kubernetes only
  • Execution: Each pipeline step runs as a Kubernetes pod
  • Scalability: Horizontal scaling via Kubernetes
  • Storage: Kubernetes volumes (PVCs) for artifact persistence
  • ML Pipeline: Defined using the Python-based kfp SDK
  • UI: Visual DAGs with logs, metadata tracking, and pipeline details
  • Artifacts: Tracked via ML Metadata (MLMD)
  • Hyperparameter tuning: Native support via Katib
  • Serving models: Built-in via KFServing / KServe
  • Experiment tracking: Integrated or via external tools like MLflow
  • Security: Istio-based authN/authZ with Kubernetes RBAC
  • Plugins: Argo templates and reusable KFP components
  • Cloud: Integrates with GCP (Vertex AI), AWS, Azure ML

Best for​

End-to-end ML pipelines, model training, hyperparameter tuning, deployment, reproducible experiments, and MLOps on Kubernetes.

Not for: General-purpose workflow orchestration or non-Kubernetes environments.

Argo Workflows​

  • Kubernetes-native workflow engine for orchestrating parallel jobs and complex workflows.
    • Including for CI. Argo CD for K8s CD.
  • Goal: container-native workflow execution using DAGs or step-based logic.
  • Orchestrator: Native Argo controller running in Kubernetes
  • Deployment: Kubernetes only
  • Execution: Each step is a Kubernetes pod defined in YAML or via SDKs
  • Scalability: Scales horizontally with Kubernetes resources
  • Storage: Artifacts passed via volumes, S3, GCS, or custom artifact repositories
  • ML Pipeline: No built-in ML abstraction; can be used as a backend (e.g., for Kubeflow)
  • UI: Visual workflow DAGs, pod status, logs, and retry logic
  • Artifacts: Managed via Argo's artifact system and volume mounts
  • Hyperparameter tuning: Not built-in; requires integration
  • Serving models: Not built-in
  • Experiment tracking: Not built-in; can integrate with MLflow or custom tools
  • Security: Kubernetes-native RBAC, plus support for pod-level service accounts and namespaces
  • Plugins: Template reuse, custom templates, and workflow templates
  • Cloud: Cloud-agnostic; supports S3, GCS, MinIO, and other cloud storage for artifacts

Best for​

Kubernetes-native workflow automation, CI/CD pipelines, data processing, and parallel job orchestration.

Not optimized for ML out-of-the-box but highly composable for custom ML platforms or backend orchestration.