Leveraging AI and ML for Automated Incident Resolution in Cloud Infrastructure
Sai Prasad Veluru · International Journal of Artificial Intelligence Data Science and Machine Learning · 2021
Modern cloud infrastructure management has become more complex as dynamic scaling, distributed systems & growing data volume create an environment that challenges operations teams. The requirement of quick & more intelligent problem identification and response becomes more critical as businesses rely more on cloud-native applications and also services. Our approach to incident management is being changed by artificial intelligence (AI) and machine learning (ML). By means of actual time analysis of vast amounts of telemetry information, artificial intelligence/machine learning algorithms may detect anomalies, project system failures & also automate their resolution processes before user impact. Beyond traditional rule-based systems, these technologies are enabling adaptive reactions that change and improve with time. From root cause research to intelligent warnings to automated remedial action, AI and machine learning techniques find application at numerous layers of the cloud stack. Automated restoration of service interruptions & more anticipatory capacity changes among practical applications help to increase their uptime and performance quantitatively. Case studies from leading cloud providers show that using AI-driven technologies into operations has greatly lowered human participation & improved their issue response times. Using AI and ML in this sector produces ultimately stronger, self-repairing infrastructure. It helps teams to focus on their strategic improvements rather than on everyday operational problems. Looking forward, it shows a trend towards more autonomous cloud configurations wherein predictive analytics & more constant learning help to enable proactive system management. Effective and trustworthy cloud operations as this shift develops rely on the cooperation of human expertise and machine intelligence