As Kubernetes adoption accelerates in cloud-native architectures, ensuring robust observability across dynamic, large-scale clusters has become a critical operational challenge. Traditional logging and monitoring systems—relying heavily on rule-based alerting and manual log inspection—struggle to scale with the volume, velocity, and complexity of modern workloads. These approaches often lead to alert fatigue, delayed incident response, and incomplete root cause analysis.This paper explores the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance observability within Kubernetes environments. By leveraging unsupervised learning for anomaly detection, natural language processing (NLP) for log parsing, and supervised models for event classification, the proposed intelligent observability framework significantly improves signal-to-noise ratios and accelerates troubleshooting processes. Through empirical evaluation on a production-grade Kubernetes testbed, the system demonstrated a 35% improvement in anomaly detection accuracy and reduced mean time to resolution (MTTR) by over 40% compared to baseline tools. These results highlight the transformative potential of AI/ML in enabling proactive, scalable, and context-aware monitoring solutions for complex cloud-native infrastructures.
Keywords: Artificial Intelligence, Logging, Monitoring, anomaly detection, kubernetes, machine learning, observability
