SQLFlow: An Extensible Toolkit Integrating DB and AI

Jun Zhou, Ke Zhang, Lin Wang, Hua Wu, Yi Wang, ChaoChao Chen

MLOSS 2023 pp. 1-9

/mloss/2023/zhou2023jmlr-sqlflow/

Abstract

Integrating AI algorithms into databases is an ongoing effort in both academia and industry. We introduce SQLFlow, a toolkit seamlessly combining data manipulations and AI operations that can be run locally or remotely. SQLFlow extends SQL syntax to support typical AI tasks including model training, inference, interpretation, and mathematical optimization. It is compatible with a variety of database management systems (DBMS) and AI engines, including MySQL, TiDB, MaxCompute, and Hive, as well as TensorFlow, scikit-learn, and XGBoost. Documentations and case studies are available at https://sqlflow.org. The source code and additional details can be found at https://github.com/sql-machine-learning/sqlflow.

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Cite

Text

Zhou et al. "SQLFlow: An Extensible Toolkit Integrating DB and AI." Machine Learning Open Source Software, 2023.

Markdown

[Zhou et al. "SQLFlow: An Extensible Toolkit Integrating DB and AI." Machine Learning Open Source Software, 2023.](https://mlanthology.org/mloss/2023/zhou2023jmlr-sqlflow/)

BibTeX

@article{zhou2023jmlr-sqlflow,
  title     = {{SQLFlow: An Extensible Toolkit Integrating DB and AI}},
  author    = {Zhou, Jun and Zhang, Ke and Wang, Lin and Wu, Hua and Wang, Yi and Chen, ChaoChao},
  journal   = {Machine Learning Open Source Software},
  year      = {2023},
  pages     = {1-9},
  volume    = {24},
  url       = {https://mlanthology.org/mloss/2023/zhou2023jmlr-sqlflow/}
}