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Samuel Madden. Subramanyam, Harihar G Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2018-01-12T20:55:58Z 2018-01-12T20:55:58Z 2017 2017 http://hdl.handle.net/1721.1/113104 1016164337 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 111-113). Data scientists go through an iterative process when building machine learning models. This process includes operations like feature selection, cross validation, model fitting, and evaluation, which are repeated until a sufficiently accurate model is produced. This thesis describes ModelDB Server and the ModelDB Spark Client, which record operations as the data scientist performs them, stores the operations and models in a central database, and exposes an API for gleaning information from the operations and models. ModelDB Server is library agnostic and it can serve as a foundation for other applications. ModelDB Spark Client is a library for the Apache Spark ML machine learning library that lets the data scientist log their operations and models with minimal code changes. ModelDB Server and Spark Client have low time and space overhead for large training dataset sizes and the overhead is independent of the dataset size. by Harihar G. Subramanyam. M.Eng. 113 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. A system for storage and analysis of machine learning operations Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="69936f81-7207-4986-b3d6-b013b90305c2"> <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>A system for storage and analysis of machine learning operations</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2017</PublicationDate> <Authors> <Author> <DisplayName>Subramanyam, Harihar G</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>Data scientists go through an iterative process when building machine learning models. This process includes operations like feature selection, cross validation, model fitting, and evaluation, which are repeated until a sufficiently accurate model is produced. This thesis describes ModelDB Server and the ModelDB Spark Client, which record operations as the data scientist performs them, stores the operations and models in a central database, and exposes an API for gleaning information from the operations and models. ModelDB Server is library agnostic and it can serve as a foundation for other applications. ModelDB Spark Client is a library for the Apache Spark ML machine learning library that lets the data scientist log their operations and models with minimal code changes. ModelDB Server and Spark Client have low time and space overhead for large training dataset sizes and the overhead is independent of the dataset size.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>