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Optimizing Data Storage for AI, Generative AI, and Machine Learning: Challenges, Architectures, and Future Direction

by Ankush Ramprakash Gautam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 73
Year of Publication: 2025
Authors: Ankush Ramprakash Gautam
10.5120/ijca2025924597

Ankush Ramprakash Gautam . Optimizing Data Storage for AI, Generative AI, and Machine Learning: Challenges, Architectures, and Future Direction. International Journal of Computer Applications. 186, 73 ( Mar 2025), 29-33. DOI=10.5120/ijca2025924597

@article{ 10.5120/ijca2025924597,
author = { Ankush Ramprakash Gautam },
title = { Optimizing Data Storage for AI, Generative AI, and Machine Learning: Challenges, Architectures, and Future Direction },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2025 },
volume = { 186 },
number = { 73 },
month = { Mar },
year = { 2025 },
issn = { 0975-8887 },
pages = { 29-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number73/optimizing-data-storage-for-ai-generative-ai-and-machine-learning-challenges-architectures-and-future-direction/ },
doi = { 10.5120/ijca2025924597 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-03-25T22:41:34.285389+05:30
%A Ankush Ramprakash Gautam
%T Optimizing Data Storage for AI, Generative AI, and Machine Learning: Challenges, Architectures, and Future Direction
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 73
%P 29-33
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In rapidly evolving fields of study such as [1] Artificial Intelligence (AI), [2] Generative AI, [3] Retrieval-Augmented Generation (RAG), and [4] Machine Learning (ML), it is crucial to store data efficiently. The ability to store, manage and retrieve large datasets has a direct impact on the performance, scalability and reliability of these applications. AI and ML depend on large amounts of data for training and inference, therefore, it needs storage solutions that are high-throughput, low-latency and cost-effective. This article aims to explore the role of data storage in AI and ML, its advantages and limitations, and presents insights from recent scholarly research. The paper also discusses various storage architectures such as cloud, hybrid, and on-premise and how they are applicable to different AI workload.

References
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Index Terms

Computer Science
Information Sciences
Data Storage
Artificial Intelligence
Generative AI
Retrieval-Augmented Generation
Machine Learning
Scalability
Performance
Data Management

Keywords

Data Storage Artificial Intelligence Generative AI Retrieval-Augmented Generation Machine Learning Scalability Performance Data Management