Machine Learning-Driven IT Cost Prediction Models in Scalable Data Platforms

Abstract

In modern IT ecosystems, accurately predicting operational costs is critical for budgeting, resource allocation, and service optimization. Traditional cost estimation models often fall short in environments characterized by high variability and scale. This paper presents a machine learning-driven approach to IT cost prediction, leveraging scalable data platforms to process large volumes of operational data in real time. We explore various regression and time series forecasting models trained on usage metrics and workload patterns, and demonstrate how distributed architectures (e.g., cloud-native platforms, big data processing engines) enhance both the accuracy and scalability of the prediction process. Experimental results show that the proposed models significantly outperform traditional baselines in terms of accuracy and adaptability, offering a viable pathway for proactive cost management in complex IT environments.