Bridging DevOps and AI: Machine Learning Models for Continuous Integration and Visual Code Quality Checks
Keywords:
DevOps, Artificial IntelligenceAbstract
As organizations increasingly adopt DevOps practices to enhance software delivery, the integration of artificial intelligence (AI) and machine learning (ML) models has emerged as a powerful solution to improve continuous integration (CI) processes. This paper explores the role of ML and computer vision in automating code quality checks within the DevOps pipeline. By leveraging visual analysis techniques, machine learning can effectively identify code defects and vulnerabilities before deployment, thus minimizing errors and reducing the time spent on manual reviews. We discuss the methodologies for implementing these technologies, the benefits of automating code quality checks, and the potential challenges organizations may face in adoption. The findings indicate that integrating AI-driven tools in CI can significantly enhance software quality, promote faster release cycles, and foster a culture of continuous improvement.
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