Cloud Computing and Machine Learning for Scalable Predictive Analytics and Automation: A Framework for Solving Real-world Problems

Abstract

This study presents a framework for harnessing cloud computing and machine learning (ML) to address real-world challenges in predictive maintenance, anomaly detection, and sentiment analysis. Leveraging cloud platforms such as AWS and Microsoft Azure, the framework processes large-scale datasets, enabling scalable and efficient solutions across various industries. In the predictive maintenance use case, a machine learning model achieved an accuracy of 92%, precision of 89%, recall of 94%, and an F1 score of 91%, demonstrating its capability to predict equipment failures with high reliability. For anomaly detection, network traffic data was analyzed, yielding a precision of 89%, recall of 85%, and an F1 score of 87%, illustrating the model's efficiency in identifying security threats. In the sentiment analysis task, a subset of 100,000 social media posts was processed, revealing that 45% of the posts were classified as positive, 35% neutral, and 20% negative. The high confidence levels in sentiment predictions, ranging from 85% to 98%, underscore the accuracy and effectiveness of the employed natural language processing (NLP) models. The results align with contemporary studies, which highlight the transformative impact of cloud-based ML systems in enhancing operational efficiency, real-time decision-making, and customer satisfaction across diverse domains (Kairo, 2024;Ucaret al., 2026; Hassan et al., 2024). These findings underscore the potential of combining cloud computing with advanced machine learning algorithms to drive automation, reduce operational costs, and optimize business processes in the digital era