Telemetry and Application Performance Monitoring: Real-Time Anomaly Detection Using AI in Retail System Monitoring
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Abstract
In today’s rapidly evolving retail landscape, digital systems are pivotal in managing operations, enhancing customer experiences, and driving business growth. Telemetry and application performance monitoring serve as essential backbones for capturing detailed system data and ensuring operational resilience. This study investigates the integration of real-time anomaly detection powered by artificial intelligence (AI) within retail monitoring frameworks. By harnessing extensive telemetry data from diverse retail applications, the proposed approach identifies subtle deviations in performance and flags potential issues as they emerge. Advanced machine learning techniques—encompassing both supervised and unsupervised algorithms—enable the system to dynamically adapt to evolving data patterns while minimizing false alerts. Continuous evaluation of critical metrics such as response times, transaction volumes, and error rates facilitates prompt detection and remediation of anomalies. Preliminary deployments indicate that real-time anomaly detection can substantially reduce system downtime and accelerate issue resolution, thereby safeguarding customer trust and ensuring uninterrupted service. Furthermore, this framework provides a scalable model that can be adapted to other digital sectors, paving the way for enhanced operational oversight. These advancements represent a significant leap forward in automated retail system management and open new avenues for proactive oversight.
Overall, the integration of AI-driven insights with telemetry data not only redefines the monitoring process but also contributes to the broader discourse on intelligent system management in modern retail environments
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