Research on Rapid Deployment Framework of AI Models for MaaS Patterns

doi:10.3901/JME.260401

Abstract

Abstract: A new wave of AI technologies, represented by embodied intelligence and large models, is accelerating integration with manufacturing and driving transformation toward greater efficiency, intelligence, and flexibility. To address challenges such as low deployment efficiency, slow response, and poor resource utilization, a rapid AI model deployment framework based on the model-as-a-service (MaaS) paradigm is proposed. Built on a cloud–edge–device architecture, the framework supports standardized model definition, decoupled packaging, fast edge deployment, and intelligent task offloading. MaaSContainer is introduced, a Docker-based packaging format. And IF-DQN is introduced, a deep reinforcement learning–based offloading strategy. Experiments and engineering practice show that the framework achieves up to 40% encapsulation rate, reduces image size by nearly 60%, maintains stable distribution latency under varying networks, and improves task scheduling performance by over 40%. The proposed framework offers a replicable, scalable solution for AI deployment in industrial intelligence, supporting the digital and intelligent transformation of manufacturing.

Key words: model-as-a-service(MaaS), model encapsulation, model distribution, AI inference task, task offloading, deep reinforcement learning

CLC Number:

TP18
TH166

LIU Xianhui, ZHAO Junlong, ZHU Chenglin, YU Deshui, SHENG Lei, ZHAO Weidong. Research on Rapid Deployment Framework of AI Models for MaaS Patterns[J]. Journal of Mechanical Engineering, 2026, 62(11): 14-29.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260401

http://www.cjmenet.com.cn/EN/Y2026/V62/I11/14

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