Leveraging Machine Learning Algorithms for Predictive Analytics in IT Operations to Optimize System Performance and Reliability

Abstract

This paper explores the application of machine learning algorithms for predictive analytics in IT operations, focusing on optimizing system performance and reliability. As IT environments grow more complex, machine learning offers a proactive approach to IT operations management (ITOM) by analyzing large datasets to predict system failures, performance issues, and security threats. The paper reviews key machine learning techniques used in ITOM, including supervised learning for resource forecasting, unsupervised learning for anomaly detection, and deep learning for time-series analysis. Additionally, the study highlights how predictive analytics enables proactive maintenance, real-time performance monitoring, and resource optimization. Despite its benefits, implementing ML-based predictive analytics presents challenges such as ensuring data quality, maintaining model accuracy, and achieving scalability. Organizations must also integrate these technologies into existing ITOM processes to maximize their effectiveness. By addressing these challenges, IT teams can leverage machine learning to enhance system performance, reduce downtime, and improve operational efficiency. This paper concludes that machine learning will continue to play an increasingly vital role in IT operations as infrastructure complexity and data generation rise.