May 10, 2017 · Big data enables ML algorithms to uncover more fine-grained patterns and make more timely and accurate predictions than ever before; on the other hand, it presents major challenges to ML such as model scalability and distributed computing.

Mar 22, 2023 · In the era of big data and connected devices of all varieties, analytics and machine learning have found ways to improve reliability, configuration, performance, fault and security management.

Apr 20, 2017 · Consequently, this paper compiles, summarizes, and organizes machine learning challenges with Big Data. In contrast to other research that discusses challenges, this work highlights the cause–effect relationship by organizing challenges according to Big Data Vs or dimensions that instigated the issue: volume, velocity, variety, or veracity.

The steadily increasing volume of data (called big data) plays an important role in machine learning, Internet of Things (IoT) and other intelligence applications.

Dec 15, 2020 · This book is intended to present the state of the art in research on machine learning and big data analytics.

Dec 1, 2018 · Big Data Science requires scalable architectures for storing and processing data. Cloud computing represents a practical and cost-effective solution for supporting Big Data storage, processing and for sophisticated analytics applications.

Feb 18, 2025 · Struggling with ML adoption challenges? Learn how to tackle data quality, integration, and cost issues with strategic solutions for seamless implementation.

Nov 4, 2023 · In this chapter, we present some of the most widely used ML algorithms in big data analytics as well as the distributed platforms typically employed for processing the data. We also present a selection of three important application domains where ML algorithms have been applied to perform big data analytics.

Jan 16, 2025 · Although big data is a breakthrough for organisations, there are several challenges for companies to face. Implementing big data solutions requires specialised skills in data engineering, data science, and machine learning.

This paper begins by providing an overview of machine learning and deep learning algorithms. The research then moves on to machine learning problems and challenges, as well as potential solutions. The investigation into deep learning principles of big data continues after that.