Integrating AI and Machine Learning in Cloud Systems for Enhanced Automation

Authors

  • Srinivasa Subramanyam Katreddy

Keywords:

AI Integration, Machine Learning, Cloud Automation, Predictive Analytics, Scalable Cloud Systems.

Abstract

The integration of AI and machine learning tools into cloud systems marks a transformative step in automation and intelligence for cloud environments. This paper explores initial methodologies for embedding AI/ML models into cloud-based infrastructures to optimize resource management, enhance data processing, and automate routine operations. The proposed approach uses containerized ML models deployed alongside scalable cloud services,enabling adaptive automation and seamless integration. Experimental studies highlight significant gains in operational efficiency, predictive analytics, and cost optimization. These
findings set a foundation for advancing AI-driven cloud systems.

References

Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of "big data" on cloud computing: Review and open research issues. Information Systems, 47, 98-115.https://doi.org/10.1016/j.is.2014.07.006

Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. NIST Special Publication 800-145

Qiu, J., Wu, Q., Ding, G., Xu, Y., & Feng, S. (2016). A survey of machine learning for big data processing. EURASIP Journal on Advances in Signal Processing, 2016(1), 116. https://doi.org/10.1186/s13634-016-0355-x

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Published

2018-12-01

How to Cite

Srinivasa Subramanyam Katreddy. (2018). Integrating AI and Machine Learning in Cloud Systems for Enhanced Automation . Journal of Computational Analysis and Applications (JoCAAA), 25(8), 67–85. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/1967

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