AIOps: Integrating AI and Machine Learning into IT Operations
Keywords:
AIOps, Artificial Intelligence, Machine Learning, IT Operations, Automated Root Cause Analysis, Future Research.Abstract
AIOps applies artificial intelligence and machine learning to transform IT processes. Artificial intelligence and machine learning enhance operational efficiency, decision-making, and IT service management including AIOps. The ideas of AIOps are discussed in this paper along with how they could improve IT operations by means of possibilities and challenges.
In IT, artificial intelligence and machine learning identify anomalies, project maintenance, and automate root cause investigation. Early performance diagnostic of IT system abnormalities is detected using ML algorithms Hardware or software issues predicted by analytics help to minimize downtime and maximize resource consumption. Rapid operational issue discovery via AI-based root cause analysis reduces MTTR and increases system reliability.
We investigate AIOps applications in telecom, healthcare, and banking. AIOps is used by banks to track transactions, spot fraud, and follow laws. With AIOps, hospital IT infrastructure management and EHR reliability become better. Through better service availability and latency, AIOps increases customer experience, resource allocation, and performance of telecom networks.
Though disruptive, AIOps adoption is challenging. For AI/ML models, data has to be complete and correct. Integrating AIOps-IT is challenging. AIOps efficacy relies on user acceptability and organizational change management as stakeholders have to be trained for operations improved by artificial intelligence.
Study addresses AIOps research areas. AIOps requires artificial intelligence-driven automation, hybrid artificial intelligence, and advanced ML. Advancement is driven by data quality, model interpretability, and ethics of AI deployment.
One significant IT development made possible by AI/ML is AIOps. Overcoming significant challenges and using fresh research will help AIOps increase operational efficiency, decision-making, and IT service management. Benefiting practitioners and researchers, this paper explores AIOps' useful applications, problems, and future research objectives.
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