AI/ML-Integrated MLOps Framework for Autonomous DevOps Pipeline Optimization: A Technical Disclosure & Validation
Year: 2025
Type: article
Abstract: Traditional DevOps pipelines rely on static rules and reactive processes, limiting scalability and reliability. This work proposes an AI/ML-integrated MLOps framework that transforms delivery pipelines into adaptive, self-optimizing systems. By learning from code changes, infrastructure signals, and operational patterns, the framework predicts deployment risks, detects anomalies, and automates decision-making. The result is a proactive, autonomous pipeline capable of reducing failures, minimizing manual intervention, and accelerating continuous delivery.
Author: Ramzy, Mostafa
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7108330178
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