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dc.contributor.advisorSamuel Madden.en_US
dc.contributor.authorVartak, Manasien_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2018-09-17T15:57:07Z
dc.date.available2018-09-17T15:57:07Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/118091
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 147-159).en_US
dc.description.abstractBuilding ML-based workflows in the real world is a trial-and-error, iterative process where an ML developer builds tens to hundreds of workflows before arriving at one that meets some task-specific acceptance criteria. This iterative process of workflow building is laborious for several reasons including the large variety of available ML models, the time required to train the workflow, difficulty keeping track of workflows built during the modeling process, and the time required for debugging trained workflows. In this thesis, we are primarily interested in two problems with the repetitive modeling process: first, how to manage ML-based workflows generated over multiple iterations of the modeling process, and second, how to efficiently debug or diagnose trained ML-based workflows. In this work, we study these questions from a systems perspective and propose novel software systems and techniques to address them. Specifically, our contributions are: 1. We propose MODELDB, a system to track provenance and performance of ML-based workflows. 2. We propose MISTIQUE, a system to store ML-based workflow intermediates in order to speed up model debugging tasks, and 3. We provide examples of new diagnostic techniques that can be designed using the data in MISTIQUE.en_US
dc.description.statementofresponsibilityby Manasi Vartak.en_US
dc.format.extent159 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT 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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleInfrastructure for model management and model diagnosisen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc1052124080en_US


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