Challenges and Opportunities in Integrating LLMs into Continuous Integration/Continuous Deployment (CI/CD) Pipelines

Challenges and Opportunities in Integrating LLMs into Continuous Integration/Continuous Deployment (CI/CD) Pipelines

연구 분야: Software Development

학회: 2024 5th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)

Large Language Models (LLMs) are powerful neural network models that can perform various language-related tasks by generating natural language conditioned on a given input or prompt. However, LLMs also pose significant challenges and risks for their development, deployment, and maintenance, such as computational cost, error and bias, and ethical and social implications. In this paper, we explore the challenges and opportunities in integrating LLMs into Continuous Integration/Continuous Deployment (CI/CD) pipelines, which are automated workflows that enable the delivery of software products or services in a fast, reliable, and consistent manner. We propose a framework for LLMOps, a specialized branch of MLOps that focuses on the development, deployment, and maintenance of LLMs. We demonstrate the use of LLMOps in a case study, where we integrate a LLM into a CI/CD pipeline for a text summarization task. We evaluate the performance, usage, and feedback of the LLM, and show that the LLM improved its quality and reliability after incorporatingthe human feedback loop. We also discuss the ethical and social implications of deploying LLMs in real-world applications, and provide recommendations and directions for future work.