Scaling DevOps Practices in Multi-Cloud Architectures for Improved Performance and Security

Abstract

The advent of artificial intelligence (AI) has revolutionized many sectors, including DevOps. As the demand for faster software development, deployment, and maintenance grows, the role of AI in automating and optimizing DevOps workflows becomes critical. This paper explores the integration of AI into DevOps practices, specifically focusing on continuous monitoring and cloud resource optimization. It discusses how AI can enhance efficiency by predicting performance issues, automating monitoring tasks, and optimizing cloud resources in real-time. Drawing on the findings of S Banik and SSM Dandyala’s 2019 study, Automated vs. Manual Testing: Balancing Efficiency and Effectiveness in Quality Assurance, the paper provides insights into how AI can be leveraged to streamline testing in DevOps while maintaining flexibility and adaptability in manual testing. Additionally, the challenges and strategic approaches to balancing AI-driven automation with human creativity are examined to foster continuous improvement in DevOps processes.