International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Automating Kubernetes Operations with AI and Machine Learning

Author(s) Varun Tamminedi
Country United States
Abstract A major development in cloud-native infrastructure management is the inclusion of artificial intelligence
and machine learning into Kubernetes activities. This article investigates how artificial intelligence-driven
automation changes conventional DevOps problems, especially in complicated multi-cluster systems
where human supervision becomes ever unsustainable. In real-world implementations, machine-learning
techniques enable predictive maintenance, intelligent resource allocation, and automated anomaly
detection in Kubernetes clusters. This article emphasizes the change from reactive to proactive operations
in which artificial intelligence systems learn from cluster behavior to maximize deployments, improve
security, and lower system downtime. The results imply that companies using Kubernetes operations
experience increases in operational efficiency, resource use, and system dependability when using
AI-enhanced Kubernetes operations experience. Moreover, this article offers a thorough architecture for
using artificial intelligence-driven automation in Kubernetes systems, together with architectural issues,
deployment techniques, and best practices. This article adds useful insights for companies trying to
upgrade their container orchestration methods and adds to the already increasing body of information on
cloud infrastructure automation.
Keywords Kubernetes Automation, Machine Learning Operations (MLOps), Cloud Infrastructure Management, Predictive Maintenance, Container Orchestration Intelligence.
Field Computer
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-20
Cite This Automating Kubernetes Operations with AI and Machine Learning - Varun Tamminedi - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.33430
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.33430
Short DOI https://doi.org/g8wkf2
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