Integration of Machine Learning Algorithms with Cloud Computing for Real-Time Data Analysis

Devidas Kanchetti; Rajesh Munirathnam; Darshit Thakkar

doi:10.55544/jrasb.3.2.46

Authors

Devidas Kanchetti Independent Researcher, USA.
Rajesh Munirathnam Independent Researcher, USA.
Darshit Thakkar Independent Researcher, USA.

DOI:

https://doi.org/10.55544/jrasb.3.2.46

Keywords:

Real-time data analysis, Machine learning algorithms, Cloud computing, Optimization problems, Optimization goals, Real-time data processing, Enhanced indexing

Abstract

As part of this study though, real-time data analysis is examined by exploring the combination of machine learning algorithms with cloud computing. It does so by defining optimization problems and solutions, as well as outlining optimization goals and directions for development. The efficiency of real time data processing ability is also amplified with the use of an amalgamation of machine learning and cloud computing though traditional systems are often associated with high failure rates and high costs of maintenance. Enhanced indexing, systematic control of information storage and retrieval, query optimization are the main benefits obtainable from this. This is because despite the challenges such as limited resources, integration has never been a problem even with challenges of data privacy. Peculiar trends such as Explainable AI, Automated ML, and Continuous Intelligence present the ability to substantially enhance operational proficiency and decision-making.

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