ML Orchestration: Why It's Time to Move Past Airflow

Apache Airflow, a widely adopted open-source project initially developed at Airbnb and now commercialized by Astronomer.io, is celebrated for its versatility in orchestrating data workflows with Direct Acyclic Graphs (DAGs) and its broad use in automating ETL processes. However, it faces significant challenges when applied to Machine Learning (ML) workflows, which require highly iterative development, local execution, comprehensive lineage tracking, and detailed visualizations—features not inherently supported by Airflow. ML workflows involve constant retraining, testing, and fine-tuning of models, necessitating a fast feedback loop and granular tracking of all contributing assets, which Airflow's architecture doesn't easily accommodate. While Airflow's extensive community support, stability, and multi-language capability are undeniable strengths, the platform's lack of advanced features for ML tasks has led many teams to seek additional tools or layers to bridge these gaps. As a result, despite its popularity, Airflow is often seen as inadequate for ML needs without substantial modifications.

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