Abstract: Cloud native infrastructure management is being transformed by Artificial Intelligence (AI) and Machine Learning (ML) techniques, often referred to as AIOps, which automate complex operations and enhance system resilience. AIOps capabilities encompass predictive maintenance, forecasting and preventing failures before they impact services, intelligent observability through the analysis of logs, metrics, and traces, and autonomous fault remediation that enables self-healing systems. These approaches are particularly valuable in Kubernetes based architectures, where dynamic microservices environments generate massive volumes of telemetry data that AI can analyze to proactively detect anomalies and performance issues.

Major cloud platforms have integrated AI driven automation into their operations toolchains. For instance, AWS DevOps Guru employs ML models to identify operational anomalies and recommend remediation actions, while Azure Monitor and Google Cloud Operations embed machine learning for intelligent alerting, performance tuning, and capacity forecasting. Open source and hybrid tools further enrich this ecosystem. KubeFlow supports ML workflows on Kubernetes, and observability frameworks like Prometheus and Elastic APM collect telemetry data that feeds into AI driven analytics and automated responses.

This article highlights how AI driven automation and AIOps practices are enhancing infrastructure reliability and efficiency, while also addressing persistent challenges. These include model drift where model accuracy degrades as systems evolve, poor data quality that undermines analytical insights, and a lack of explainability in AI decisions which complicates trust and broader adoption of AIOps solutions.

Keywords: AI-Driven Infrastructure, AIOps, Cloud-Native Systems, Predictive Maintenance

| DOI: 10.17148/IARJSET.2022.91122