Self-Tuning Observability: Meta- Learning for Autonomous Monitoring and Alert Fatigue Reduction in Evolving Data Platforms

Abstract

Observability in evolving data platforms is increasingly limited by the inability of static monitoring configurations to keep pace with changing workloads, dependency structures, and signal behavior. Alert fatigue has become a major operational problem because excessive low-value notifications reduce operator attention, slow triage, and weaken trust in observability systems. The main gap is the lack of frameworks that can autonomously retune monitoring thresholds, suppression policies, and correlation logic as platform conditions change. This matters because noisy or outdated alerting policies can overwhelm operators while still failing to maintain stable monitoring quality. This article presents a meta-learning framework for self-tuning observability that adapts monitoring configuration across logs, metrics, traces, and event streams in evolving data platforms. The results show progressive stabilization of alert volume across adaptation cycles and stronger monitoring precision with greater alert fatigue reduction than static and heuristic configuration strategies. The study demonstrates that meta-learning can provide an effective foundation for autonomous observability control in dynamic data environments.