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Informative Article | Computer Science and Information Technology | Volume 15 Issue 1, January 2026 | Pages: 1574 - 1575 | United States
AI-Driven Deployment and Cloud Migration Strategies for Stable Software Releases
Abstract: The growing complexity of modern software systems has made manual deployment difficult to manage at scale. Artificial intelligence offers practical support across the deployment lifecycle, from traffic control to rollback decisions. This article discusses how AI supports blue and green deployment strategies through automated traffic orchestration, anomaly detection, predictive rollback, performance tuning, and user experience monitoring. It outlines the role of machine learning, deep learning, generative models, and reinforcement learning in improving release safety and system reliability. Common operational use cases such as load testing, circuit breakers, chaos engineering, caching, and cloud migration are also examined. The discussion shows how AI-enabled deployment reduces downtime, lowers release risk, and supports continuous improvement in production environments. The AI-Integrated Enhanced Deployment Strategy leverages machine learning and intelligent automation to revolutionize software delivery, optimizing CI/CD pipelines for greater speed, reliability, and accuracy. By automating testing, proactively detecting errors, and dynamically managing resources, this approach reduces deployment risks, overcomes infrastructure complexities, and ensures faster, more secure release cycles. This approach is critical for modern MLOps, allowing organizations to manage model drift and ensure AI models are consistently updated in production environments.
Keywords: AI deployment, blue green strategy, cloud migration, automated rollback, system stability
How to Cite?: Santosh Kumar Nayak, "AI-Driven Deployment and Cloud Migration Strategies for Stable Software Releases", Volume 15 Issue 1, January 2026, International Journal of Science and Research (IJSR), Pages: 1574-1575, https://www.ijsr.net/getabstract.php?paperid=SR26126220323, DOI: https://dx.dx.doi.org/10.21275/SR26126220323