  Industrial AI systems are mostly end-to-end machine learning (ML) workflows.
A typical recommendation or business intelligence system includes many online
micro-services and offline jobs. We describe SQLFlow for developing such
workflows efficiently in SQL. SQL enables developers to write short programs
focusing on the purpose (what) and ignoring the procedure (how). Previous
database systems extended their SQL dialect to support ML. SQLFlow
(https://sqlflow.org/sqlflow ) takes another strategy to work as a bridge over
various database systems, including MySQL, Apache Hive, and Alibaba MaxCompute,
and ML engines like TensorFlow, XGBoost, and scikit-learn. We extended SQL
syntax carefully to make the extension working with various SQL dialects. We
implement the extension by inventing a collaborative parsing algorithm. SQLFlow
is efficient and expressive to a wide variety of ML techniques -- supervised
and unsupervised learning; deep networks and tree models; visual model
explanation in addition to training and prediction; data processing and feature
extraction in addition to ML. SQLFlow compiles a SQL program into a
Kubernetes-native workflow for fault-tolerable execution and on-cloud
deployment. Current industrial users include Ant Financial, DiDi, and Alibaba
Group.

Databases;Machine Learning