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Improving the Reliability of Cloud-Based Pre-Trained Machine Learning Models

Cummaudo, Alex , Improving the Reliability of Cloud-Based Pre-Trained Machine Learning Models, Ph.D. thesis, A2I2 Applied Artificial Intelligence Institute, Deakin University.

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Title Improving the Reliability of Cloud-Based Pre-Trained Machine Learning Models
Author Cummaudo, AlexORCID iD for Cummaudo, Alex orcid.org/0000-0001-7878-6283
Institution Deakin University
School A2I2 Applied Artificial Intelligence Institute
Degree type Research doctorate
Degree name Ph.D.
Thesis advisor Vasa, RajeshORCID iD for Vasa, Rajesh orcid.org/0000-0003-4805-1467
Grundy, John
Summary This thesis identified non-trivial implications to software quality resulting from evolving pre-trained machine learning models served through cloud computing platforms. The study's technological solutions help resolve challenges faced by developers dependent on such 'plug-and-play' technology, both through a novel software architecture that enhances integration robustness and improved service documentation techniques.
Language eng
Indigenous content off
Description of original 369 p.
Copyright notice ┬ęThe author
Free to Read? Yes
Use Rights Creative Commons Attribution Share Alike licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30152816

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Created: Thu, 24 Jun 2021, 10:17:46 EST by Leanne Swaneveld

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.