Optimizing machine learning pipelines for cost and performance using cloud

Abstract–This article explores comprehensive strategies for optimizing machine learning pipelines in cloud environments, focusing on IP protection systems. It addresses the critical challenges of balancing performance, cost, and scalability while maintaining robust security measures. The discussion encompasses various optimization techniques, including cloud infrastructure management, batch processing implementations, asynchronous model invocation, and memory management strategies. Through examination of real-world implementations and research findings, the article demonstrates how organizations can leverage cloud-native services, advanced compression techniques, and intelligent resource allocation to enhance their ML operations. The article provides practical insights into achieving cost-effective scaling while maintaining high-performance standards, offering valuable guidance for engineers and architects working with cloud-based machine learning systems.