Embedding Artificial Intelligence in Deployment Pipelines: A Framework for Predictive and Autonomous DevOps

Jyostna Seelam

Abstract: As modern software deployment pipelines escalate in complexity and velocity, traditional reactive DevOps practices struggle to maintain efficiency and reliability. The imperative for intelligent, proactive decision-making has become critical. This paper introduces a novel framework for embedding Artificial Intelligence (AI) into Continuous Integration/Continuous Delivery (CI/CD) pipelines, aiming to establish predictive and autonomous DevOps capabilities. By leveraging machine learning models trained on comprehensive pipeline data, including historical deployment logs, telemetry, and code change patterns, the proposed framework enhances traditional automation by adding context-aware adaptability. It outlines strategic AI integration points across the entire pipeline lifecycle, encompassing pre-commit risk assessment, intelligent deployment gating, real-time anomaly detection, and autonomous rollback strategies. This research envisions a self-optimizing, AI-driven deployment ecosystem that significantly reduces deployment failures, enhances release reliability, and facilitates a seamless transition towards truly autonomous operations. The framework's detailed methodology provides a roadmap for organizations to implement more robust and efficient software delivery processes.

Keywords: Artificial Intelligence, DevOps, Deployment Pipelines, Machine Learning, Predictive Analytics

How to Cite?: Jyostna Seelam, "Embedding Artificial Intelligence in Deployment Pipelines: A Framework for Predictive and Autonomous DevOps", Volume 12 Issue 5, May 2023, International Journal of Science and Research (IJSR), Pages: 2715-2723, https://www.ijsr.net/getabstract.php?paperid=SR2506133231, DOI: https://dx.doi.org/10.21275/SR2506133231