Articles
| Open Access | An Integrated MLOps Framework for Robust, Scalable Deployment of Large Language Models Across Domains
Dr. Tobias Müller , Institute for Renewable Energy Systems, Technical University of Munich, GermanyAbstract
The rapid proliferation and success of large language models (LLMs) across domains — from natural language processing, finance, medicine to multimodal tasks — highlight their transformative potential for research, industrial applications, and societal impact. However, scaling LLM deployment in real-world, production-grade environments introduces significant challenges in reproducibility, maintainability, performance optimization, and quality assurance. This article proposes a comprehensive, conceptual MLOps‑centric framework for the deployment and lifecycle management of LLMs, integrating continuous integration/continuous delivery (CI/CD) pipelines in cloud-based settings, combined with domain‑aware evaluation and governance strategies. Drawing on extensive literature — including surveys of LLM architectures and applications (Minaee et al., 2024; Naveed et al., 2023; Pahune & Chandrasekharan, 2023; Zhao et al., 2023), domain‑specific use cases in finance (Lee et al., 2024), medicine (Gao et al., 2023; Dada et al., 2024), information retrieval (Zhu et al., 2023), multilingual models (Yuan et al., 2023), and multimodal expansions (Wang et al., 2024) — as well as recent work on MLOps practices and tool ecosystems (Chandra, 2025; Berberi et al., 2025; Zarour et al., 2025; Kazmierczak et al., 2024), we articulate the architectural components, workflow stages, evaluation metrics, risk‑mitigation strategies, and domain‑adaptive customization necessary for sustainable deployment. We discuss in detail the benefits, limitations, and future directions, including adaptability for specialized domains, governance, reproducibility, and cross‑domain interoperability. Our framework aspires to serve as a reference blueprint for researchers, engineers, and stakeholders seeking to operationalize LLMs effectively and responsibly.
Keywords
Large Language Models, MLOps, CI/CD pipelines, model deployment
References
Chandra, R. (2025). OPTIMIZING LLM PERFORMANCE THROUGH CI/CD PIPELINES IN CLOUD-BASED ENVIRONMENTS. International Journal of Applied Mathematics, 38(2s), 183-204.
John Snow Labs. (2024). Introduction to Large Language Models (LLMs): An Overview of BERT, GPT, and Other Popular Models. Available online: https://www.johnsnowlabs.com/introduction-to-large-language-models-llms-an-overview-of-bert-gpt-and-other-popular-models/ (accessed on 14 September 2024).
Gao, Y.; Baptista-Hon, D. T.; Zhang, K. (2023). The inevitable transformation of medicine and research by large language models: The possibilities and pitfalls. MEDCOMM-Future Med, 2, 1–2.
Minaee, S.; Mikolov, T.; Nikzad, N.; Chenaghlu, M.; Socher, R.; Amatriain, X.; Gao, J. (2024). Large language models: A survey. arXiv:2402.06196.
Zhu, Y.; Yuan, H.; Wang, S.; Liu, J.; Liu, W.; Deng, C.; Chen, H.; Dou, Z.; Wen, J. R. (2023). Large language models for information retrieval: A survey. arXiv:2308.07107.
Lee, J.; Stevens, N.; Han, S. C.; Song, M. (2024). A survey of large language models in finance (FinLLMs). arXiv:2402.02315.
Yuan, F.; Yuan, S.; Wu, Z.; Li, L. (2023). How Multilingual is Multilingual LLM? arXiv:2311.09071.
Dada, A.; Bauer, M.; Contreras, A. B.; Koraş, O. A.; Seibold, C. M.; Smith, K. E.; Kleesiek, J. (2024). CLUE: A Clinical Language Understanding Evaluation for LLMs. arXiv:2404.04067.
Wang, W.; Chen, Z.; Chen, X.; Wu, J.; Zhu, X.; Zeng, G.; Luo, P.; Lu, T.; Zhou, J.; Qiao, Y.; et al. (2024). VisionLLM: Large language model is also an open-ended decoder for vision-centric tasks. In Proceedings of the 37th Conference on Neural Information Processing Systems, New Orleans, LA, USA, 6–10 December 2024.
Naveed, H.; Khan, A. U.; Qiu, S.; Saqib, M.; Anwar, S.; Usman, M.; Akhtar, N.; Barnes, N.; Mian, A. (2023). A comprehensive overview of large language models. arXiv:2307.06435.
Pahune, S.; Chandrasekharan, M. (2023). Several categories of large language models (LLMs): A short survey. arXiv:2307.10188.
Zhao, W. X.; Zhou, K.; Li, J.; Tang, T.; Wang, X.; Hou, Y.; … Wen, J. (2023). A survey of large language models. arXiv.
Berberi, L.; Kozlov, V.; Nguyen, G.; Díaz, J. S.; Calatrava, A.; Moltó, G.; … García, Á. L. (2025). Machine learning operations landscape: platforms and tools. Artificial Intelligence Review, 58(6).
Zarour, M.; Alzabut, H.; Alsarayrah, K. (2025). MLOps best practices, challenges and maturity models: A systematic literature review. Information and Software Technology, 107733.
Kazmierczak, J.; Salama, K.; Huerta, V. (2024, August 28). MLOps: Continuous delivery and automation pipelines in machine learning. Google Cloud.
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Copyright (c) 2025 Dr. Tobias Müller
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