Integrating Computer Vision with DevOps: Automating Infrastructure Monitoring and Visual Diagnostics
Published 25-11-2023
Keywords
- Computer Vision,
- DevOps,
- Infrastructure Monitoring,
- Machine Learning,
- Visual Diagnostics
- Automated Responses,
- IT Management ...More
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
The rapid evolution of IT infrastructure management necessitates innovative approaches to monitor, diagnose, and respond to anomalies in real-time. This paper explores the integration of computer vision techniques within DevOps practices to automate infrastructure monitoring and visual diagnostics. By leveraging machine learning models and image processing algorithms, DevOps teams can enhance their ability to detect potential issues, streamline workflows, and improve overall system reliability. The study discusses various computer vision applications in infrastructure monitoring, such as anomaly detection, pattern recognition, and automated reporting. Furthermore, it highlights the challenges associated with implementing these technologies and offers insights into future directions for research and development. The findings emphasize the potential of computer vision to transform traditional DevOps methodologies into more responsive and intelligent systems, ultimately contributing to improved operational efficiency and reduced downtime.
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References
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