DAG Card is the new Model Card
This work aims to improve transparency and reproducibility in real-world ML deployments for practitioners, though it is incremental as it builds on existing Model Card ideas.
The paper proposes DAG Cards as documentation for machine learning pipelines, addressing the shift towards data-centric AI by extending the Model Card concept to cover pre- and post-training processes, and provides an open-source implementation for generating these cards from code.
With the progressive commoditization of modeling capabilities, data-centric AI recognizes that what happens before and after training becomes crucial for real-world deployments. Following the intuition behind Model Cards, we propose DAG Cards as a form of documentation encompassing the tenets of a data-centric point of view. We argue that Machine Learning pipelines (rather than models) are the most appropriate level of documentation for many practical use cases, and we share with the community an open implementation to generate cards from code.