Paper Title
SCALABLE ALGORITHMS FOR MACHINE LEARNING IN BIG DATA ENVIRONMENTS: CHALLENGES AND INNOVATIONS

Abstract
Big data and machine learning innovations are transforming new landscapes, heralding variables but revealing surprisingly demanding scenarios. Too much information makes computation quantity, classification, speed, accuracy, and respect difficult for, and complicates power, resilience, and insight. At the same time, scaling up ML fashion in technology faces obstacles of computational difficulties, special execution difficulties, strange behaviors, information protection, and complex integration looking at these difficulties so requires improved planning, stronger performance, and significant interactions between innovation and entrepreneurial aspirations. Networked computing, cloud enhancements, and real-time processing are needed to overcome these obstacles and obtain scalable, sophisticated nuggets of knowledge from discrete datasets. Keywords - Big Data Challenges, Scalable Machine Learning, Data Processing, Distributed Computing