AIOps in Action: Streamlining IT Operations Through Artificial Intelligence
Keywords:
Predictive Maintenance, Automated Root Cause Analysis, Artificial Intelligence, System Integration, AIOps, Data Quality, Machine Learning, IT Operations, Future Research, Anomaly Detection.Abstract
AI and machine learning revolutionize IT processes in AIOps. AI and ML improve operational efficiency, decision-making, and IT service management, including AIOps. This article discusses AIOps and how it could improve IT operations through opportunities and obstacles.
Machine learning and AI detect abnormalities, maintain projects, and automate root cause research in IT. ML algorithms diagnose IT system issues early. Analytical predictions of hardware or software faults reduce downtime and maximize resource use. AI-based root cause analysis minimizes MTTR and improves system reliability by discovering operational issues quickly.
We study telecom, healthcare, and banking AIOps. AIOps helps banks detect fraud, track transactions, and comply with legislation. AIOps improves hospital IT infrastructure management and EHR reliability. AIOps improves telecom network performance, customer experience, and resource allocation by improving service availability and latency.
Though disruptive, AIOps adoption is hard. Complete, accurate data is needed for AI/ML models. AIOps-IT integration is hard. Stakeholders must be trained for AI-enhanced operations, therefore user acceptability and organizational change management are crucial.
Study covers AIOps research. AIOps requires AI-driven automation, mixed AI, and advanced ML. Data quality, model interpretability, and AI deployment ethics drive progress.
AI/ML enables AIOps, a major IT advancement. AIOps can improve operational efficiency, decision-making, and IT service management by overcoming major obstacles and leveraging new research. This paper discusses AIOps' uses, issues, and research goals for practitioners and researchers.
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