What Airflow, Prefect, Dagster, and Temporal each do for AI systems, when your ML pipeline complexity and team maturity dictate which orchestrator fits best, and how to apply a structured decision framework to select the right tool for production AI data pipelines.
Asset-based orchestration, software-defined assets, and Dagster's lineage model.
What dynamic DAGs, sensors, and fan-out/fan-in patterns do for AI systems, when ML workflows require data-aware scheduling and conditional branching across training and serving stages, and how to apply these patterns in production AI data pipelines.
Unit testing DAGs, SLA monitoring, and alerting on pipeline failures.
