Infrastructure✓ Mathematical
◆ The PatternDAGs, retries, backfills, and trigger strategies
Orchestrators manage the when, how, and what-if of ML workflows. Airflow, Prefect, and Dagster define DAGs with dependency resolution, automatic retries, and backfill capabilities. Good orchestration means your retraining runs reliably at 2 AM without you.
// Interactive — DAG dependency graph
# Python — Airflow DAG for retraining from airflow import DAG from airflow.operators.python import PythonOperator from datetime import datetime dag = DAG("retrain_model", schedule_interval="0 2 * * *", start_date=datetime(2025, 1, 1)) ingest = PythonOperator(task_id="ingest", python_callable=ingest_data, dag=dag) train = PythonOperator(task_id="train", python_callable=train_model, dag=dag) eval_ = PythonOperator(task_id="eval", python_callable=evaluate, dag=dag) deploy = PythonOperator(task_id="deploy", python_callable=deploy_model, dag=dag) ingest >> train >> eval_ >> deploy
Pattern bridge: Orchestration DAGs structure ML workflows the same way computation graphs structure neural networks — directed, acyclic, and dependency-ordered. In markets, systematic trading rules follow the same sequential logic.