Unifying DevOps and MLOps Pipelines Via AI-driven Observability: A Mixed-Methods Study

Abstract

The analysis explores artificial intelligence-based observability as an operational solution to unite technological structures between Development and Operations systems and Machine Learning Operations systems. Enterprise environments that use machine learning require advanced strategies to manage the deployment and monitoring of ML models together with conventional software systems because they have become increasingly complex. The study investigates how artificial intelligence enables observable functions which support smooth integration of operations and automation between DevOps and MLOps development cycles. The research methodology includes a mixed approach that starts with a literature study which combines practitioner interviews with DevOps and MLOps professionals and a prototype AI observability framework development. The developed prototype utilizes machine learning analytics to detect anomalies along with roots cause identification and automated alert functions during evaluation on hybrid CI/CD and ML pipelines. AI-driven observability provides comprehensive application and model performance visibility while shortening the detection and resolution periods of system failures and making operations more effective through proactive monitoring and automated diagnosis and intelligent remedy strategies. The technology allows stakeholders to monitor dashboards which merge metrics and logs stemming from software applications and ML models therefore facilitating domain alignment between software developers and data scientists. This study demonstrates that AI observation solutions serve as vital infrastructure to unite MLOps practices with DevOps operations by connecting developers with data scientists and operators in their work. This solution solves the essential problems that stem from separated workflows in addition to unclear visibility and inconsistent operational performance. Organizations implementing intelligent observability measure in ML-integrated systems will accomplish faster deployment timelines along with model dependability maintenance and system stability which produces stronger and more scalable AI-driven production environments.