AI Powered Cloud Native Platforms for Intelligent DevOps and Real Time Enterprise Data Systems

Raquel Urtasun

doi:10.15662/IJRAI.2025.0806033

Authors

Raquel Urtasun Senior Systems Engineer, United Kingdom Author

DOI:

https://doi.org/10.15662/IJRAI.2025.0806033

Keywords:

AI-powered platforms, cloud-native architecture, intelligent DevOps, real-time data systems, Kubernetes, microservices, MLOps, AIOps, event-driven architecture, serverless computing, predictive analytics, anomaly detection, self-healing systems, enterprise automation, distributed systems, streaming data, observability, scalable infrastructure

Abstract

AI-powered cloud-native platforms are transforming modern DevOps and enterprise data systems by embedding intelligence directly into infrastructure, pipelines, and real-time decision workflows. By combining containerized microservices, Kubernetes orchestration, serverless computing, and event-driven architectures with machine learning and large language models, organizations can automate operations, enhance system resilience, and accelerate innovation. Intelligent DevOps leverages predictive analytics for anomaly detection, automated root cause analysis, self-healing infrastructure, and optimized CI/CD pipelines, reducing downtime and operational overhead.

Simultaneously, real-time enterprise data systems powered by streaming platforms and distributed data architectures enable continuous insights across business operations. AI-driven observability, adaptive scaling, intelligent resource allocation, and automated security monitoring create resilient, self-optimizing ecosystems. These platforms unify data engineering, MLOps, and cloud-native best practices to deliver scalable, secure, and intelligent digital infrastructure capable of supporting dynamic enterprise workloads. The convergence of AI, cloud-native principles, and real-time data processing marks a shift toward autonomous, insight-driven enterprise systems.

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