ml-pipeline-orchestration

Automate end-to-end ML workflows with Apache Airflow DAGs, Kubeflow Pipelines, Prefect flows, Dagster ops and assets, ZenML, and Argo Workflows. Covers pipeline scheduling (cron, event-driven, data-driven triggers), task dependency management, fan-out/fan-in patterns, parameter passing via XComs and artifacts, retry and error handling, pipeline monitoring and alerting, conditional branching, caching, artifact reuse, pipeline versioning, reproducibility, and CI/CD integration. Use when building or scheduling ML pipelines, wiring training/evaluation/ deployment steps into a DAG, choosing or migrating between orchestrators, or debugging pipeline failures and retries.

Version

1.0

License

Apache-2.0