Design an End-to-End ML Pipeline Orchestration Platform — System Design Interview Practice
Design a platform that orchestrates end-to-end ML workflows from data ingestion through model deployment, with DAG-based pipeline definitions, caching, and reproducibility guarantees. Work through the requirements, architecture trade-offs, and an interactive design review.
Requirements and concepts to consider
- Define ML pipelines as DAGsRequirement
- Orchestrate data processing, training, and evaluation stepsRequirement
- Cache intermediate pipeline artifactsRequirement
- Support pipeline parameterization and schedulingRequirement
- Track pipeline runs with lineage metadataRequirement
- Handle pipeline failures with retry and alertingRequirement
- Support distributed execution across compute backendsRequirement
- Integrate with version control for pipeline definitionsRequirement
- mlopsConcept to explore
- pipeline orchestrationConcept to explore
- workflowConcept to explore
- dagConcept to explore
- kubeflowConcept to explore
- automationConcept to explore