Automation and Life Cycle Management Optimization of Large-Scale Machine Learning Platforms

Abstract

With the continuous deepening of intelligent technology, machine learning technology has been adopted in many fields, making the management and maintenance of large machine learning systems particularly complex. Automated operations and optimization of the entire system lifecycle have become the core components for improving operational efficiency and reducing maintenance costs. This study aims to examine the architecture design and component functions of large-scale machine learning systems, and analyze the challenges encountered in current automation implementation, resource allocation, parameter optimization, and system maintenance, and propose corresponding improvement measures. These measures include the refinement of processes, intelligent management of resources, establishment of an automated model evaluation system, and the creation of an intelligent operation and maintenance system. These suggestions will help improve the operational performance and management level of the system, and create more efficient and scalable machine learning application platforms for various enterprises.