  Automated machine learning makes it easier for data scientists to develop
pipelines by searching over possible choices for hyperparameters, algorithms,
and even pipeline topologies. Unfortunately, the syntax for automated machine
learning tools is inconsistent with manual machine learning, with each other,
and with error checks. Furthermore, few tools support advanced features such as
topology search or higher-order operators. This paper introduces Lale, a
library of high-level Python interfaces that simplifies and unifies automated
machine learning in a consistent way.
