AMLTK: A Modular AutoML Toolkit in Python

MCML Authors

Matthias Feurer

Prof. Dr.

Thomas Bayes Fellow

* Former Thomas Bayes Fellow

Abstract

Machine Learning is a core building block in novel data-driven applications. Practitioners face many ambiguous design decisions while developing practical machine learning (ML) solutions. Automated machine learning (AutoML) facilitates the development of machine learning applications by providing efficient methods for optimizing hyperparameters, searching for neural architectures, or constructing whole ML pipelines (Hutter et al., 2019). Thereby, design decisions such as the choice of modelling, pre-processing, and training algorithm are crucial to obtaining well-performing solutions. By automatically obtaining ML solutions, AutoML aims to lower the barrier to leveraging machine learning and reduce the time needed to develop or adapt ML solutions for new domains or data.<br>Highly performant software packages for automatically building ML pipelines given data, so-called AutoML systems, are available and can be used off-the-shelf. Typically, AutoML systems evaluate ML models sequentially to return a well-performing single best model or multiple models combined into an ensemble. Existing AutoML systems are typically highly engineered monolithic software developed for specific use cases to perform well and robustly under various conditions...

article

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The Journal of Open Source Software

9.100. Aug. 2024.

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Authors

E. Bergman • M. Feurer • A. Bahram • A. R. Balef • L. Purucker • S. Segel • M. Lindauer • F. Hutter • K. Eggensperger

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Links

DOI

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Research Area

 A1 | Statistical Foundations & Explainability