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Software Engineering Practices for AI-Driven Systems: From Development to Deployment (MLOps Perspective)

Dr. Vimal Raja Gopinathan

Abstract

The high rate of Artificial Intelligence (AI) transfer to different industries has caused the necessity of strong software engineering practices to guarantee the well-development, implementation and support of AI-driven systems. The paper describes how the conventional software engineering concepts, such as testing, versioning, continual integration and delivery (CI/CD), and lifecycle management, can be applied to AI systems, specifically through the concept of Machine Learning Operations (MLOps). MLOps is needed as it addresses the challenges of handling the end-to-end lifecycle of machine learning models, including their creation and the deployment in production systems. The paper points out the main difficulties in the adaptation of traditional software engineering practices to AI systems, including the stability of the models, version control, and providing constant performance monitoring. It also suggests viable tactics and remedies to curb such obstacles, provides information on how MLOps can enhance the process of AI model rollout and maintenance to enhance system reliability and performance altogether.

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 8 No. 1 (2025): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2025.0801002

Pages

13493-13500

Published

February 6, 2025
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Dr. Vimal Raja Gopinathan (%2025). Software Engineering Practices for AI-Driven Systems: From Development to Deployment (MLOps Perspective). International Journal of Science, Research and Technology , Vol. 8 No. 1 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 13493-13500. https://doi.org/10.15662/IJSRAT.2025.0801002

References

[1] Arseniev, D. G., Baskakov, D. E., Kasurinen, J., Shkodyrev, V. P., & Mergasov, A. (2023). Software Engineering Principles Apply to Artificial Intelligence Systems. Lecture Notes in Networks and Systems, 151–158. https://doi.org/10.1007/978-3-031-20875-1_14
[2] Cherukuri, B. R. (2024). AI-powered personalization: How machine learning is shaping the future of user experience. International Journal of Science and Research Archive, 12(01), 3111–3126. https://doi.org/10.30574/ijsra.2024.12.1.0961
[3] Garg, S., Pundir, P., Rathee, G., Gupta, P. K., Garg, S., & Ahlawat, S. (2021). On Continuous Integration / Continuous Delivery for Automated Deployment of Machine Learning Models using MLOps. 2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Laguna Hills, CA, USA, 25-28. https://doi.org/10.1109/AIKE52691.2021.00010
[4] Kokina, J., & Davenport, T. H. (2017). The Emergence of Artificial Intelligence: How Automation is Changing Auditing. Journal of Emerging Technologies in Accounting, 14(1), 115–122. https://doi.org/10.2308/jeta-51730
[5] King, T. M., Arbon, J., Santiago, D., Adamo, D., Chin, W., & Shanmugam, R. (2019). AI for Testing Today and Tomorrow: Industry Perspectives. 2019 IEEE International Conference On Artificial Intelligence Testing (AITest), Newark, CA, USA, 81-88. https://doi.org/10.1109/AITest.2019.000-3
[6] Kaur, S., et al. (2020). Medical diagnostic systems using artificial intelligence (AI) algorithms: Principles and perspectives. IEEE Access, 8, 228049-228069. https://doi.org/10.1109/ACCESS.2020.3042273
[7] Moura, L. S. (2022). A Cloud-Native AI-Driven Enterprise Architecture Supporting Intelligent Operations Organizational Resilience and Data-Driven Decision Making with SAP Integration. International Journal of Research and Applied Innovations, 5(2), 9753–6758. https://doi.org/10.15662/IJRAI.2022.0502004
[8] Ojika, F. U., Owobu, W. O., Abieba, O. A., Esan, O. J., Ubamadu, B. C., & Daroajimba, A. I. (2021). A conceptual framework for AI-driven digital transformation: Leveraging NLP and machine learning for enhanced data flow in retail operations. ICONIC Research and Engineering Journals, 4(9), 189. https://doi.org/10.30574/ire.2021.1702633
[9] Sandeep, S. R., Ahamad, S., Saxena, D., Srivastava, K., Jaiswal, S., & Bora, A. (2021). To understand the relationship between Machine learning and Artificial intelligence in large and diversified business organisations. Materials Today: Proceedings, 56(4). https://doi.org/10.1016/j.matpr.2021.11.409
[10] Wang, L., Liu, Z., Liu, A., & Tao, F. (2021). Artificial intelligence in product lifecycle management. The International Journal of Advanced Manufacturing Technology, 114(3-4), 771–796. https://doi.org/10.1007/s00170-021-06882-1