# DAG Card is the new Model Card

arXiv: https://arxiv.org/abs/2110.13601 · HTML: https://mlsanity.com/paper/2110.13601/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 24 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [Changing the World by Changing the Data](https://mlsanity.com/paper/2105.13947/) (2021)
- [You Do Not Need a Bigger Boat: Recommendations at Reasonable Scale in a (Mostly) Serverless and Open Stack](https://mlsanity.com/paper/2107.07346/) (2021)
- ["Are you sure?": Preliminary Insights from Scaling Product Comparisons to Multiple Shops](https://mlsanity.com/paper/2107.03256/) (2021)

## Built on by (most cited first, 5 of 5)

- [A domain-specific language for describing machine learning datasets](https://mlsanity.com/paper/2207.02848/) (2022)
- [Beyond NDCG: behavioral testing of recommender systems with RecList](https://mlsanity.com/paper/2111.09963/) (2021)
- [Reasonable Scale Machine Learning with Open-Source Metaflow](https://mlsanity.com/paper/2303.11761/) (2023)
- [TechOps: Technical Documentation Templates for the AI Act](https://mlsanity.com/paper/2508.08804/) (2025)
- [Fine-Grained Traceability for Transparent ML Pipelines](https://mlsanity.com/paper/2601.14971/) (2026)

## Abstract (arXiv)

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.