The Important of Big Data in Machine Learning
Luu Tan Thanh *
Hanoi University of Science, Vietnam National University, Hanoi, Vietnam.
Nguyen Quang Dat
Hanoi University of Science, Vietnam National University, Hanoi, Vietnam.
Vu Hoang
Hanoi University of Science, Vietnam National University, Hanoi, Vietnam.
To Hien Huy Hieu
Le Quy Don Technical University, Vietnam.
*Author to whom correspondence should be addressed.
Abstract
The integration of Big Data and Machine Learning is revolutionizing various industries by enabling smarter decision-making, enhancing automation, and improving predictive analytics. Big Data plays a pivotal role in Machine Learning by providing large volumes of diverse, real-time data that fuel the learning process. The availability of vast amounts of data allows Machine Learning models to be trained on complex patterns, leading to better accuracy, improved generalization, and more reliable predictions. Moreover, Big Data facilitates the use of advanced techniques like deep learning, which require massive datasets for tasks such as image recognition, natural language processing, and recommendation systems.
However, the role of Big Data extends beyond mere volume; it also offers variety, providing diverse datasets essential for building more robust and adaptable Machine Learning models. The continuous stream of real-time data enables dynamic learning, while data diversity enhances model versatility. Despite these benefits, challenges such as data quality, processing scalability, and privacy concerns must be addressed. In summary, Big Data significantly amplifies the capabilities of Machine Learning by enhancing model performance, driving innovations, and enabling applications across domains such as healthcare, finance, retail, and autonomous systems.
Keywords: Big data, machine learning (ML), model performance, autonomous systems