Anomaly Detection for Incident Response at Scale illustrates Walmart’s revolutionary approach to managing operational risks through AI-based technologies. Authored by a team led by Hanzhang Wang, this study unveils AI Detect and Respond (AIDR), a system that enhances incident response capabilities across various teams. This paper details the design, implementation and results of AIDR in improving incident handling.

• Real-Time Monitoring: Via multiple layers of ML and statistical models, AIDR effectively provides real-time monitoring of Walmart’s systems to promptly detect anomalies.
• Reduction in Incident Detection Time: The deployment of AIDR reduced the mean time to detect (MTTD) major incidents by over seven minutes, demonstrably enhancing the efficacy of Walmart’s response processes.
• Customizability and Focus on Users: The system offers self-onboarding tools and customization options, making it adaptable to specific team needs and user-friendly.
• Future Plans: The paper discusses plans to expand the tool’s capabilities, including further integration with root cause analysis tools.

Opinion: The AIDR project is an impressive example of how AI can be leveraged to create practical solutions that significantly impact operational efficiency. The successful implementation of such a system at Walmart serves as a model for other corporations wishing to enhance their anomaly detection capabilities. This also identifies the potential for extended AI use in proactive incident management.