Completion Date

Winter 4-14-2024

Document Type


Degree Name

Master of Science (MS)

Program or Discipline Name

Computer Information Sciences

First Advisor

Majid Shaalan


This paper presents a comprehensive framework for enhancing user experience in mobile applications through the integration of deep learning systems. The proposed system design encompasses various components, including data collection and preprocessing, model development and training, integration with mobile applications, dataset management service, model training service, model serving, hyperparameter optimization, metadata and artifact store, and workflow orchestration. Each component is meticulously designed with a focus on scalability, efficiency, isolation, and critical analysis. Innovative design principles are employed to ensure seamless integration, usability, and automation. Additionally, the paper discusses distributed training service design, advanced optimization techniques, and decision criteria for hyperparameter optimization library selection. Furthermore, the implementation details of model serving, metadata and artifact store, and workflow orchestration are provided, along with practical guidelines for model release, monitoring, and optimization. The paper concludes with a roadmap outlining the path to producing the software solution, emphasizing integration strategies, deployment innovations, and experimentation in production. Overall, this paper serves as a comprehensive guide for researchers and practitioners seeking to enhance user experience through deep learning systems in mobile applications.



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