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Automated Machine Learning
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AbstractIn the past few years automated machine learning (AutoML) has gained a lot of traction in the data science and machine learning community. AutoML aims at reducing the partly repetitive work of data scientists and enabling domain experts to construct machine learning pipelines without extensive knowledge in data science. This chapter presents a comprehensive review of the current leading AutoML methods and sets AutoML in an industrial context. To this extent we present the typical components of an AutoML system, give an overview over the stateof-the-art and highlight challenges to industrial application by presenting several important topics such as AutoML for time series data, AutoML in unsupervised settings, AutoML with multiple evaluation criteria, or interactive human-in-the-loop methods. Finally, the connection to Neural Architecture Search (NAS) is presented and a brief review with special emphasis on hardware-aware NAS is given.
Springer Nature Switzerland
Title: Automated Machine Learning
Description:
AbstractIn the past few years automated machine learning (AutoML) has gained a lot of traction in the data science and machine learning community.
AutoML aims at reducing the partly repetitive work of data scientists and enabling domain experts to construct machine learning pipelines without extensive knowledge in data science.
This chapter presents a comprehensive review of the current leading AutoML methods and sets AutoML in an industrial context.
To this extent we present the typical components of an AutoML system, give an overview over the stateof-the-art and highlight challenges to industrial application by presenting several important topics such as AutoML for time series data, AutoML in unsupervised settings, AutoML with multiple evaluation criteria, or interactive human-in-the-loop methods.
Finally, the connection to Neural Architecture Search (NAS) is presented and a brief review with special emphasis on hardware-aware NAS is given.
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