Optimizing Cloud-Based Distributed Systems for Real-Time Machine Learning Model Deployment and Scaling

Abstract

The rapid evolution of machine learning (ML) models and the surge in data volumes necessitate scalable and efficient deployment strategies. Cloud-based distributed systems offer on-demand scalability and resource flexibility, making them ideal for real-time ML model deployment and scaling. This paper explores optimization techniques for cloud-based distributed systems to enhance the deployment and scaling of ML models in real-time applications. We examine the integration of distributed systems and ML within cloud environments, focusing on scalable training and inference mechanisms. Key considerations such as task partitioning, communication overhead, fault tolerance, and resource optimization are discussed. Furthermore, we review auto-scaling techniques, highlighting advancements and challenges in dynamically adjusting resources to meet fluctuating demands. The paper also delves into the application of machine learning for cloud resource provisioning, emphasizing dynamic allocation based on real-time usage patterns. By synthesizing current research and practices, this study provides insights into effectively leveraging cloud-based distributed systems for real-time ML model deployment and scaling.