AI and Machine Learning Advances

Cloud-native architectures have emerged as the basis of digital services in the modern context of their scaling, modularity, and continuous deployment capabilities. The growing sophistication of distributed microservice environments posed a major problem with regard to ensuring system reliability and governance. Conventional monitoring systems based on fixed limits and reactive management of incident alerts are not usually appropriate in dynamic cloud applications. The paper examines the use of artificial intelligence as a means of improving reliability governance with Artificial Intelligence (AI) driven error budgeting and predictive monitoring in cloud-native environments. The study uses the qualitative analytical method that is supported by a series of case studies of AI-based monitoring systems, predictive DevOps automation, and interdependent reliability control of Kubernetes systems. The results show that the machine learning models can be used to greatly enhance the anomaly detection, incident prediction, and proactive reliability management process by processing massive amounts of telemetry information produced by distributed cloud systems. The AI-based forecasting models also make such predictions ahead of time, so organisations predict Service Level Objectives (SLO) violation and give timely service to the affected users, and the reliability teams can respond proactively (by scaling resources or redistributing traffic). Moreover, the AI-based DevOps automation and autonomous remediation systems cut down on the operational overhead and enhance the resilience of systems. Results indicate 17–28% increases in SLO compliance and MTTR using AI-based predictions. The machine learning models that were aided by AI minimised false positives and enhanced web-based anomaly detection rates in distributed microservice settings. The forecasting with predictive error budget also facilitated earlier intervention whereby reliability teams could anticipate cascading failures and resource allocation can be done proactively. In contrast to the previous researches that focus on monitoring only, this study incorporates predictive error budgeting, coupled with frameworks of governance level automation. The analysis summarises that prediction analytics and smart observability systems, as well as automated remediation frameworks, should be implemented to ensure the effective establishment of proactive reliability governance in cloud-native infrastructures.