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MLProvCodeGen: A Tool for Provenance Data Input and Capture of Customizable Machine Learning Scripts

dc.contributor.authorMustafa, Tarek Al
dc.contributor.authorKönig-Ries, Birgitta
dc.contributor.authorSamuel, Sheeba
dc.contributor.editorKönig-Ries, Birgitta
dc.contributor.editorScherzinger, Stefanie
dc.contributor.editorLehner, Wolfgang
dc.contributor.editorVossen, Gottfried
dc.date.accessioned2023-02-23T14:00:19Z
dc.date.available2023-02-23T14:00:19Z
dc.date.issued2023
dc.description.abstractOver the last decade Machine learning (ML) has dramatically changed the application ofand research in computer science. It becomes increasingly complicated to assure the transparency and reproducibility of advanced ML systems from raw data to deployment. In this paper, we describe an approach to supply users with an interface to specify a variety of parameters that together provide complete provenance information and automatically generate executable ML code from this information. We introduce MLProvCodeGen (Machine Learning Provenance Code Generator), a JupyterLab extension to generate custom code for ML experiments from user-defined metadata. ML workflows can be generated with different data settings, model parameters, methods, and trainingparameters and reproduce results in Jupyter Notebooks. We evaluated our approach with two ML applications, image and multiclass classification, and conducted a user evaluation.en
dc.identifier.doi10.18420/BTW2023-72
dc.identifier.isbn978-3-88579-725-8
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/40382
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBTW 2023
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-331
dc.subjectProvenance Management
dc.subjectCode Generation
dc.subjectMachine Learning
dc.subjectJupyterLab
dc.subjectJupyter Notebooks
dc.subjectReproducibility
dc.titleMLProvCodeGen: A Tool for Provenance Data Input and Capture of Customizable Machine Learning Scriptsen
dc.typeText/Conference Paper
gi.citation.endPage1067
gi.citation.publisherPlaceBonn
gi.citation.startPage1059
gi.conference.date06.-10. März 2023
gi.conference.locationDresden, Germany

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