AI-Powered Risk Forecasting for Disruptive Operations in DevOps and Data Engineering Pipelines

Abstract

The research examines how AI-driven risk-predicting systems can be used in DevOps and data engineering pipelines to achieve greater operational stability and effectiveness. With the help of machine learning models, the research will assess predictive analytics that can minimize the rates of failures, enhance the deployment frequency, and the time of issues resolutions. Some of the issues, such as data quality, model complexity, and real-time processing, are identified, and ways of addressing these issues are given. The results underscore the appropriateness of AI in the risk management process by being proactive, further leading to more consistent and efficient DevOps activities. Suggestions to make the AI introduction in DevOps environments even better are offered.