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