Cloud-native technologies ai-enhanced observability: machine learning pipeline for Kubernetes log analytics in EKS environments
Year: 2025
Type: article
Abstract: This article explores the integration of artificial intelligence and machine learning techniques into Kubernetes log analytics, with a specific focus on environments. As organizations increasingly adopt container orchestration for mission-critical applications, traditional monitoring approaches have proven inadequate for addressing the complexity, scale, and ephemeral nature of cloud-native architectures. The article shows how AI-driven log analytics can transform observability through automated anomaly detection, predictive analytics, and human-AI collaboration frameworks. By leveraging machine learning algorithms, natural language processing, and real-time data processing architectures, these advanced solutions enable organizations to transition from reactive troubleshooting to proactive management. The article presents implementation frameworks, maturity models, and practical case studies demonstrating how AI-enhanced observability significantly improves operational efficiency, reduces mean time to resolution, and enhances system reliability in complex Kubernetes deployments.
Author: Mohammed, Naseer Ahamed
Institution: Fair Isaac Corporation (United States)
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7083588660
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