Data Infrastructure Requirements for Leveraging Machine Learning in Generative AI Applications

  • Authors

    • Dr. Zainab Abdullahi Department of Education, Kano State University, Nigeria. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2019PI3T9M

    Published 02-05-2019

  • Generative AI, Machine Learning, Data Infrastructure, Scalable Data Storage, Data Processing, Cloud Computing, Gans, Vaes, Data Governance, Distributed Computing, AI Ethics, Cloud Platforms, Real-Time Data Pipelines

    Issue

    Vol. 2 No. 1 (2019)

    Section

    Articles

    How to Cite

    [1]
    Z. Abdullahi, “Data Infrastructure Requirements for Leveraging Machine Learning in Generative AI Applications”, IJAIDT, vol. 2, no. 1, pp. 01–10, Feb. 2019, doi: 10.67228/30713315/IJAIDT-2019PI3T9M.

  • Abstract

    As generative AI continues to gain traction across industries, the importance of robust data infrastructure to support machine learning (ML) workflows becomes increasingly critical. This paper explores the key data infrastructure requirements for leveraging ML in generative AI applications. It covers essential components such as scalable data storage, data preprocessing, and real-time data pipelines, while also addressing the challenges of handling large, unstructured datasets. We examine the role of distributed computing and cloud platforms in supporting the computational needs of generative AI models like GANs and VAEs. Additionally, we discuss the importance of data governance, security, and compliance in ensuring the success and ethical application of generative AI. Through real-world use cases, we highlight how industries like healthcare, entertainment, and finance are benefiting from advanced data infrastructure to drive innovation in generative AI. Finally, the paper outlines best practices for building and optimizing data infrastructure to ensure that organizations can fully leverage the potential of machine learning in generative AI applications.

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