Towards Converged MLOps and SRE: Adaptive AI-Driven Reliability Strategies in Cloud Environments

Abstract

Converged MLOps and SRE bring together model deployment, monitoring, reliability, automation, and scalability into a unified standard for production-grade, near-continuous AI operations and infrastructure. This study describes how MLOps and Site Reliability Engineering convergence, combined with the power of adaptive AI technologies, can greatly improve system reliability, scalability and automation in cloud-native scenarios. The literature highlights the move towards automation and predictive reliability that is AI-driven, as well as policy-based operations. This study employed an explanatory mixed-method research design and qualitative and quantitative secondary data to discuss how MLOps and Site Reliability Engineering converge by using adaptive, AI-driven reliability approaches in contemporary cloud computing settings. The study also establishes that MLOps and SRE together with adaptive AI hold a lot of promise in improving the reliability of systems running in the cloud. The results include greater automation, predictive fault identification, and recovery. It provides real-world advantages, current limitations, and upcoming recommendations, which promote a powerful, scalable, and smart model of next-generation cloud-native operations.