Lale: Consistent Automated Machine Learning

TL;DR

Lale is a Python library that unifies and simplifies automated machine learning by providing consistent interfaces and supporting advanced features like topology search.

Contribution

It introduces a high-level, unified Python interface for automated machine learning, addressing inconsistencies and supporting advanced pipeline features.

Findings

Provides a consistent API for automated machine learning

Supports advanced features like topology search and higher-order operators

Simplifies pipeline development for data scientists

Abstract

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.