Machine Learning and Cloud Computing Case Study for Online App Usage Tracking

Abstract

Scalability, data processing, and real-time insights are not present in traditional online app usage tracking. ML can overcome these restrictions and open new possibilities with cloud data, in accordance with this study. In our case study, cloud data and ML boosted how a fitness software tracked user activity hours. Cloud systems store and manage massive amounts of user activity logs, device data, and network traffic of program usage. ML systems can learn trends, predict user behavior, and gain insights from this data. Fitness app data accuracy, user experience, and scalability improved with these technologies. On other web app domains, this method can improve user engagement tracking and personalization.