Automation✓ Mathematical
◆ The PatternComposing training, validation, and deployment into reproducible DAGs
An ML pipeline is a directed acyclic graph (DAG) of steps: data ingestion → preprocessing → training → evaluation → deployment. Each step is versioned, cacheable, and independently retriable. Kubeflow, Airflow, Vertex, and SageMaker Pipelines are the main orchestrators.
// Interactive — ML pipeline DAG
| Platform | Strengths | Best for |
|---|---|---|
| Kubeflow Pipelines | K8s-native, portable | Cloud-agnostic teams |
| Apache Airflow | Mature, huge ecosystem | Data engineering + ML |
| Vertex AI Pipelines | Managed, GCP-integrated | Google Cloud shops |
| SageMaker Pipelines | Managed, AWS-integrated | AWS shops |
Pattern bridge: ML pipelines enforce the same disciplined workflow as cross-validation — each step has defined inputs and outputs, preventing data leakage between stages.