Multi-tenant architectures present unique reliability challenges for Site Reliability Engineering teams, requiring solutions beyond traditional manual interventions and static rules. This article explores the integration of artificial intelligence into cloud-native SRE pipelines to enhance fault prediction, incident management, and automated remediation in distributed environments. The architecture encompasses time-series models for anomaly detection, NLP systems for incident classification, reinforcement learning for automated remediation, and adaptive resource management across tenant boundaries. The implementation strategies and real-world applications, the paper demonstrates how ML-augmented SRE practices transform reliability operations while addressing challenges including model drift, interpretability, data quality, and fairness considerations. The integration of machine learning with established reliability practices creates a foundation for autonomous, self-healing platforms that maintain resilience at scale while delivering consistent experiences across diverse tenant populations.
Susanta Kumar Sahoo (2025) studied this question.