AI-driven observability: Transforming monitoring and alerting in CI/CD platforms

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.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-30
DOI
https://doi.org/10.5281/zenodo.17205368
Primary Topic
Geochemistry and Elemental Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-driven observability: Transforming monitoring and alerting in CI/CD platforms

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Elemental Analysis
article

AI-driven observability: Transforming monitoring and alerting in CI/CD platforms

article en

Abstract

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.

Zenodo (CERN European Organization for Nuclear Research)
Walmart (United States) (US)
Openalex Percentile: Top 45%
Geochemistry and Elemental Analysis
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AI-driven observability: Transforming monitoring and alerting in CI/CD platforms · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS