Requirements for Data Infrastructure in Generative AI Applications to Utilize Machine Learning
Dr. Sivaraju Kuraku
University of the Cumberlands, Williamsburg, KY 40769, USA
DOI: 10.63665/ijmlaidse-y1f2a003
View / Download Full Article (PDF)Abstract
With generative AI becoming increasingly common in more locations, there's a greater need than ever to have a strong data infrastructure supportive of machine learning workflows. The paper studies the basic requirements of data infrastructure for the implementation of machine learning into generative AI applications. This includes important components such as the preparation of data, creation of real-time working data pipelines, and accommodation of increasing data. It also handles the problems that occur when one has to work on lots of messy data. We then review how generative AI models, including VAE and GAN, can be further enhanced using cloud and distributed computing. We discuss the use of generative AI in a useful yet moral way and the need to follow the regulations and security of data. We showcase the improvements that can be expected by generative AI when advanced data infrastructure is used in healthcare, entertainment, and finance. The paper concludes by listing the best ways for businesses to improve their data infrastructure in support of generative AI applications to learn more from machine learning.
Keywords
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.
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