Deployment and Optimization Strategy of Machine Learning Model Based on Cloud Computing

This article aims to explore the deployment strategy and optimization method of ML (Machine Learning) model based on cloud computing, so as to improve the performance of the model in the cloud computing environment, reduce the cost and ensure the security. Aiming at this goal, firstly, the challenges faced by the current ML model deployment are analyzed, including poor scalability, low resource utilization, high cost and security risks. Based on this, a complete set of deployment strategy and optimization system is proposed. The system covers micro-service architecture, distributed training, model quantification and pruning, hardware acceleration, cloud service selection and resource allocation. The results show that micro-service architecture has obvious advantages in scalability and management convenience compared with single architecture. Distributed training technology can significantly shorten the model training time; Reasonable cloud service selection and resource allocation strategies reduce operating costs. The research results verify the effectiveness of the proposed strategy and optimization method, and provide strong technical support for practical application.