The application of artificial intelligence (AI) in the manufacturing sector is growing rapidly and diversely, with industries such as machinery manufacturing, semiconductor production, and automation integration actively incorporating AI technologies into their processes. Enterprises urgently require flexible remote deployment solutions that seamlessly integrate adaptive AI models into production lines, thereby increasing efficiency in model deployment and replacement while reducing overall costs. To address these needs, this study proposes a platform for remote, automated deployment, and remodeling of AI models, which not only enhances the adaptability of production line models but also minimizes downtime by resolving the time-consuming nature of traditional deployment practices. Leveraging containerization (packaging applications with their dependencies into portable units) and automated deployment mechanisms, the proposed platform enables remote updates and remodelings, effectively overcoming challenges associated with introducing AI in machine manufacturing, semiconductor manufacturing, and automation integration—especially in scenarios requiring online model adjustments. Compared to previous research, this study prioritizes industry-driven requirements, providing a remote automated AI model deployment solution that better aligns with enterprises’ operational goals. The findings underscore the potential for significantly reducing production costs and improving operational efficiency, paving the way for smarter, more responsive manufacturing processes.
Tung et al. (Wed,) studied this question.
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