Enhancing Distributed Systems for Real-Time Machine Learning Model Deployment and Management

Abstract

The integration of machine learning (ML) models into distributed systems has become pivotal for applications requiring real-time data processing and decision-making. This paper investigates methodologies to enhance distributed architectures for the efficient deployment and management of ML models in real-time environments. We explore the challenges associated with latency, scalability, and fault tolerance, and propose solutions leveraging edge computing, federated learning, and dynamic orchestration. Through empirical evaluations, we demonstrate the efficacy of the proposed approaches in optimizing real-time ML workflows.