AI model full life cycle management method and system based on cloud native

By adopting a cloud-native AI model lifecycle management method and system, the problems of environmental fragmentation and resource scheduling of AI models in cloud-native environments are solved, realizing automated management and efficient deployment of models, and improving resource utilization and model stability.

CN121636078APending Publication Date: 2026-03-10SHANDONG LANGCHAO YUNTOU INFORMATION TECH CO LTD
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Patent Information

Application Number
CN202511507616.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-21
Publication Date
2026-03-10

AI Technical Summary

Technical Problem

In cloud-native environments, the development, training, and deployment of AI models suffer from problems such as environmental fragmentation, version conflicts, low resource utilization, and insufficient monitoring capabilities, which lead to the inability to detect and intervene in model degradation in a timely manner and unreasonable resource scheduling.

Method used

This paper presents a cloud-native AI model lifecycle management method and system. Through a hybrid cloud infrastructure of multi-tenant development platform, containerized environment and Kubernetes ecosystem, it realizes model code encapsulation, automated CI/CD pipeline, canary release, multi-dimensional monitoring and dynamic resource scheduling, and combines dynamic weight factor algorithm to schedule heterogeneous resources.

Benefits of technology

It enables automated management of the entire lifecycle of AI models, improves resource utilization and management efficiency, ensures model stability and reliability, reduces development and deployment costs, and enhances system manageability and security.

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Abstract

The invention relates to the cross technical field of artificial intelligence operation and maintenance and cloud native computing, in particular to an AI model full life cycle management method and system based on cloud native, and the method comprises the steps: constructing a containerized interactive development environment, and generating an invariable OCI mirror image containing a model code and a dependency library; a versioning model registration center of a three-layer storage architecture is adopted, and strong consistency management of model files and metadata is achieved; an automatic CI / CD assembly line is driven through a declarative workflow engine, and event triggering and timing scheduling are supported; the reasoning service deployment module based on the service grid realizes gray release and elastic capacity expansion and contraction; a multi-dimensional monitoring index system is established, and model performance attenuation and data drift are detected in real time; and designing an adaptive resource scheduling algorithm, and dynamically optimizing heterogeneous computing resource allocation. The resources can be dynamically adjusted according to the actual load, and high performance and high availability of the model service are ensured.
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