Improving Predictive Accuracy with Cloud-Based Machine Learning Models for Big Data Analytics

The present paper aims at focusing on improving the capabilities of the machine learning models with the help of cloud solutions for big data processing. The emphasis is on utilization of computing power of cloud, based, and storage-volume for machine learning algorithms used in processing large data. The research advances new strategies for enhancing the specificity of the predictive models and relates them to cloud computing for model’s training, hyperparameters optimization, and real-time data analysis. Main conclusions show that cloud ml is more accurate to the true values than on-premise solutions particularly in cases of high-D and unstructured data environments. Thus, the study contributes to the current research by presenting findings regarding enhanced machine learning fostered by cloud computing for big data. It is important to have a look at the literature review when assessing these findings as it has ramifications for industries that heavily rely on predictive analytics such as finance, healthcare, and marketing among others, this therefore presents a future direction for enhancement of decision making based on the data collected.