AI-Driven Software Engineering: Optimizing Distributed Systems for Scalable Machine Learning Workflows

Abstract

This paper explores the integration of artificial intelligence techniques into software engineering practices to optimize distributed systems for scalable machine learning (ML) workflows. As ML models grow in complexity and data volume, traditional system design approaches struggle to meet the demands of performance, scalability, and resource efficiency. We propose an AI-driven framework that leverages predictive analytics, automated resource management, and intelligent scheduling to enhance distributed computing environments. The study examines key challenges in distributed ML systems, including data partitioning, workload balancing, fault tolerance, and latency optimization. Through a combination of simulation and real-world case studies, we demonstrate how AI-based optimization strategies improve system throughput, reduce training time, and enhance resource utilization. The results highlight the potential of combining software engineering principles with AI-driven decision-making to build resilient and efficient ML infrastructures. This work contributes a structured approach for designing next-generation distributed systems capable of supporting large-scale machine learning applications.