Walmart Global Tech, USA.
World Journal of Advanced Research and Reviews, 2025, 26(01), 366-384
Article DOI: 10.30574/wjarr.2025.26.1.1073
Received on 26 February 2025; revised on 03 April 2025; accepted on 05 April 2025
AI-driven observability is transforming how organizations monitor and maintain CI/CD platforms, enabling a shift from reactive troubleshooting to proactive system management. By integrating machine learning with traditional monitoring tools, companies like Walmart are achieving significant improvements in alert quality, detection speed, and incident prevention. This article explores the limitations of conventional monitoring approaches and the potential of AI to address these challenges through pattern recognition, adaptive baselines, and predictive capabilities. It examines Walmart's implementation journey, the technical architecture required for effective AI-driven observability, and the importance of human-AI collaboration in maximizing operational effectiveness. The evolution toward business-aligned observability and observability-driven development represents a fundamental reimagining of how reliability engineering operates in cloud-native environments.
AI-Driven Observability; Alert Fatigue, Predictive Maintenance; Causality Analysis; Human-AI Collaboration
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Jithendra Prasad Reddy Baswareddy. AI-driven observability: Transforming monitoring and alerting in CI/CD platforms. World Journal of Advanced Research and Reviews, 2025, 26(01), 366-384. Article DOI: https://doi.org/10.30574/wjarr.2025.26.1.1073.
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0