IMLMA: An Intelligent Algorithm for Model Lifecycle Management with Automated Retraining, Versioning, and Monitoring

Abstract

With the rapid adoption of artificial intelligence (AI) in domains such as power, transportation, and finance, the number of machine learning and deep learning models has grown exponentially. However, challenges such as delayed retraining, inconsistent version management, insufficient drift monitoring, and limited data security still hinder efficient and reliable model operations. To address these issues, this paper proposes the Intelligent Model Lifecycle Management Algorithm (IMLMA). The algorithm employs a dual-trigger mechanism based on both data volume thresholds and time intervals to automate retraining, and applies Bayesian optimization for adaptive hyperparameter tuning to improve performance. A multi-metric replacement strategy, incorporating MSE, MAE, and R², ensures that new models replace existing ones only when performance improvements are guaranteed. A versioning and traceability database supports comparison and visualization, while real-time monitoring with stability analysis enables early warnings of latency and drift. Finally, hash-based integrity checks secure both model files and datasets. Experimental validation in a power metering operation scenario demonstrates that IMLMA reduces model update delays, enhances predictive accuracy and stability, and maintains low latency under high concurrency. This work provides a practical, reusable, and scalable solution for intelligent model lifecycle management, with broad applicability to complex systems such as smart grids.