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Tim Kraska. Shang, Zeyuan. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2020-11-03T20:29:18Z 2020-11-03T20:29:18Z 2020 2020 https://hdl.handle.net/1721.1/128304 1202001398 This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 63-66). Statistical knowledge and domain expertise are key to extract actionable insights out of data, yet such skills rarely coexist together. In Machine Learning, high-quality results are only attainable via mindful data preprocessing, hyperparameter tuning and model selection. Domain experts are often overwhelmed by such complexity, de-facto inhibiting a wider adoption of ML techniques in other fields. Existing libraries that claim to solve this problem, still require well-trained practitioners. Those frameworks involve heavy data preparation steps and are often too slow for interactive feedback from the user, severely limiting the scope of such systems. In this work we present Alpine Meadow, a first Interactive Automated Machine Learning tool. What makes the system unique is not only the focus on interactivity, but also the combined systemic and algorithmic design approach; on one hand we leverage ideas from query optimization, on the other we devise novel selection and pruning strategies combining cost-based Multi-Armed Bandits and Bayesian Optimization. We evaluate the system on over 300 datasets and compare against other AutoML tools, including the current NIPS winner, as well as expert solutions. Not only is Alpine Meadow able to significantly outperform the other AutoML systems while -- in contrast to the other systems -- providing interactive latencies, but also outperforms in 80% of the cases expert solutions over data sets we have never seen before. by Zeyuan Shang. S.M. S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 66 pages eng Massachusetts Institute of Technology MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Democratizing data science through interactive curation of ML pipelines Thesis application/pdf 2020-11-03T20:29:16Z Publication Master EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="e34695f4-9b9a-4404-b30f-58f291a82add"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Democratizing data science through interactive curation of ML pipelines</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2020</PublicationDate> <Authors> <Author> <DisplayName>Shang, Zeyuan.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Statistical knowledge and domain expertise are key to extract actionable insights out of data, yet such skills rarely coexist together. In Machine Learning, high-quality results are only attainable via mindful data preprocessing, hyperparameter tuning and model selection. Domain experts are often overwhelmed by such complexity, de-facto inhibiting a wider adoption of ML techniques in other fields. Existing libraries that claim to solve this problem, still require well-trained practitioners. Those frameworks involve heavy data preparation steps and are often too slow for interactive feedback from the user, severely limiting the scope of such systems. In this work we present Alpine Meadow, a first Interactive Automated Machine Learning tool. What makes the system unique is not only the focus on interactivity, but also the combined systemic and algorithmic design approach; on one hand we leverage ideas from query optimization, on the other we devise novel selection and pruning strategies combining cost-based Multi-Armed Bandits and Bayesian Optimization. We evaluate the system on over 300 datasets and compare against other AutoML tools, including the current NIPS winner, as well as expert solutions. Not only is Alpine Meadow able to significantly outperform the other AutoML systems while -- in contrast to the other systems -- providing interactive latencies, but also outperforms in 80% of the cases expert solutions over data sets we have never seen before.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>