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Directed Acyclic Graphs for AI and MLOps Orchestration

The source provides a comprehensive overview of the Directed Acyclic Graph (DAG) framework, establishing it as the essential architectural pattern for automating and scaling workflows within Artificial Intelligence (AI) and Machine Learning Operations (MLOps). It explains that a DAG uses directed nodes (tasks) and acyclic edges (dependencies) to ensure workflows proceed sequentially without loops, which is crucial for reproducibility and system reliability. The text maps the entire MLOps lifecycle, from data ingestion to model deployment, onto the transactional nodes of a DAG, detailing operational benefits like idempotency and parallel execution. Finally, it contrasts generalist orchestrators like Apache Airflow with specialized, cloud-native tools such as Kubeflow Pipelines and asset-focused tools like Dagster and Prefect, emphasizing that the choice of framework impacts scalability and data lineage management.