Autonomous Model Lifecycle Management: AI Systems for Self-Monitoring, Updating, and Optimization
Abstract
Managing the lifecycle of artificial intelligence models, including monitoring, updating, and performance optimization, presents significant operational challenges. This research explores autonomous model lifecycle management systems capable of monitoring performance, detecting degradation, and initiating adaptive updates without human intervention. It evaluates automated retraining pipelines, performance validation mechanisms, and deployment optimization strategies. Experimental findings demonstrate that autonomous lifecycle management significantly improves system reliability, reduces maintenance overhead, and enhances long-term performance stability. The study also analyzes governance considerations and risk mitigation strategies. A self-managing lifecycle architecture integrating monitoring, validation, and adaptive optimization is proposed. The findings establish autonomous lifecycle management as essential for enabling scalable and sustainable AI deployment.
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