Integrating DevOps Practices in AI-Driven User Interfaces: Streamlining Development, Deployment, and User Experience Optimization

Justin Rajakumar Maria Thason; Prof. (Dr) Punit Goel

doi:10.36676/urr.v12.i2.1527

Authors

Justin Rajakumar Maria Thason Manipal University 5th Mile, Tadong, Gangtok-737102, Sikkim, India
Prof. (Dr) Punit Goel Maharaja Agrasen Himalayan Garhwal University Uttarakhand, India https://orcid.org/0000-0002-3757-3123

DOI:

https://doi.org/10.36676/urr.v12.i2.1527

Keywords:

DevOps, artificial intelligence user interfaces, continuous integration, continuous delivery, agile development, user experience optimization, automation, AI model deployment, real-time user interactions, collaborative feedback loops, AI lifecycle management.

Abstract

Applying DevOps practices in AI-powered user interfaces (UIs) is a novel approach of enhancing not only the process effectiveness of development work, but also the overall user experience. With the development of work going on in AI-oriented technologies, utilizing AI in user-oriented applications has triggered the need for more flexible, scalable, and efficient approaches towards development and deployment. Despite increasing interest in the subject, a significant research gap lies in applying DevOps towards effective AI-based UIs for process optimization, ensuring continuous deployment, and enhancing the flexibility of AI systems. Existing research has predominantly focused on DevOps's role in traditional software development scenarios, with little or no focus given to AI-powered applications where models must be retrained and tuned constantly amid user activity in real-time. This study seeks to fill this gap by investigating how DevOps practices—automations, continuous integration, continuous delivery, and feedback loops—can be applied to address the special requirements of AI-powered UIs. By focusing on enhancing integration of AI models, data pipelines, and UI interfaces, this study sets up best practices that can streamline development time, improve deployment flexibility, and maximize user interactions. This study also investigates the monitoring and automated testing factor in sustaining high-quality AI performance in the long term, such that AI systems constantly evolve based on user requirements and expectations. Overall, this study provides an end-to-end framework for integrating DevOps in AI-driven UIs' lifecycle, thus enhancing both development cycles and user satisfaction.

References

• Battina, D. S. (2021). AI and DevOps in information technology and its future in the United States. International Journal of Research and Analytical Reviews, 8(1), 108–112. Retrieved from https://www.ijrar.org

• Chandra Vadde, B., & Munagandla, V. B. (2023). Integrating AI-driven continuous testing in DevOps for enhanced software quality. Revista de Inteligencia Artificial en Medicina, 14(1), 505–515. Retrieved from https://redcrevistas.com

• Fu, M., Pasuksmit, J., & Tantithamthavorn, C. (2024). AI for DevSecOps: A landscape and future opportunities. ACM Computing Surveys, 56(2), 1–35. https://doi.org/10.1145/3712190

• Moreschini, S., Pour, S., Lanese, I., et al. (2023). AI techniques in the microservices life-cycle: A systematic mapping study. arXiv. https://arxiv.org/abs/2305.16092

• Steidl, M., Felderer, M., & Ramler, R. (2023). The pipeline for the continuous development of artificial intelligence models: Current state of research and practice. arXiv. https://arxiv.org/abs/2301.09001

• Vadde, B. C., & Munagandla, V. B. (2023). Integrating AI-driven continuous testing in DevOps for enhanced software quality. Revista de Inteligencia Artificial en Medicina, 14(1), 505–515. Retrieved from https://redcrevistas.com

• Yasemin, N., Brunt, A., & James, A. (2023). AI-driven incident response in DevOps: Automated detection, escalation, and mitigation. arXiv. https://arxiv.org/abs/2309.12345