Integrating Machine Learning Models for Real-Time IT Network Optimization

Abstract

The integration of Machine Learning (ML) models into IT network optimization has become a pivotal approach to improving network performance, enhancing resource allocation, and reducing latency. By leveraging ML algorithms, such as reinforcement learning, supervised learning, and deep learning, IT networks can achieve real-time adjustments based on dynamic traffic patterns, user behaviors, and anomaly detection. This paper explores the various machine learning techniques applied to network optimization, focusing on their ability to adapt to real-time changes and enhance overall network efficiency. Key challenges such as data quality, model accuracy, and scalability are addressed, alongside the benefits of automation in managing complex IT networks. The study demonstrates that ML-driven optimization contributes to a more agile, self-regulating network infrastructure capable of meeting the demands of modern enterprise environments.