AIOps in DevOps: Leveraging Artificial Intelligence for Operations and Monitoring
This research work investigates the significant effects of implementing AIOps (Artificial Intelligence for IT Operations) on many aspects of system management and IT infrastructure performance improvement, showing how AI-driven solutions have a big influence on IT operations and hold the promise of more dependable and effective systems.
Abstract
This research work investigates the significant effects of implementing AIOps (Artificial Intelligence for IT Operations) on many aspects of system management and IT infrastructure performance improvement. AIOps led to a 15% improvement in anomaly detection accuracy, a 30% decrease in system outages, a 50% increase in incident management effectiveness, and a 15% reduction in cloud infrastructure costs. These developments show how AI-driven solutions have a big influence on IT operations and hold the promise of more dependable and effective systems. A hybrid approach combining supervised and unsupervised learning technique is proposed for anomaly detection, showcasing improved accuracy compared to traditional rule-based methods. Predictive analytics using time series analysis and forecasting models enables proactive issue resolution by detecting resource constraints and preventing performance bottlenecks. NLP techniques are explored for automating incident management, resulting in significant time savings and improved accuracy.