ML Pipeline Orchestration - Interactive Visualization

ML pipelines coordinate multiple steps - data ingestion, validation, preprocessing, training, evaluation, and deployment - in a guaranteed dependency order. DAG-based orchestrators like Apache Airflow, Prefect, and Kubeflow Pipelines manage this automatically: only completed tasks unblock their downstream tasks, and failures halt the DAG without propagating silently. This interactive demo shows a real ML pipeline DAG with 6 nodes, live status updates, and injectable failures to illustrate how orchestrators handle partial failures.

• DAG (Directed Acyclic Graph): tasks run only after all their dependencies complete - guarantees correct ordering
• Apache Airflow uses Python DAGs scheduled by the scheduler and executed by workers
• Prefect uses Python flows/tasks with a UI and automatic retry logic
• Kubeflow Pipelines runs each step as a Kubernetes pod - portable and cloud-native
• Inject failure at any stage: downstream tasks are automatically skipped, not retried blindly
• Runtime tracking: see how long each stage takes to identify pipeline bottlenecks

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.