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Artificial intelligence for IT operations (AIOps) has become a hot topic, often described as the future of IT resilience. However, a lot of discussions end at the strategy level without going into specifics about how to really construct it. The real value of AIOps comes from implementing predictive monitoring that integrates with existing enterprise monitoring stacks, applies machine learning to operational data and automates both analysis and response.
This article provides a deep dive into those mechanics: Integrating AIOps with enterprise monitoring tools, building ML models that learn from system logs and telemetry and automating alert correlation for faster root cause analysis. Along the way, we’ll explore data streaming pipelines, anomaly detection models and the automation frameworks that make predictive monitoring actionable.
The majority of businesses currently have an ecosystem of robust monitoring tools, such as Dynatrace or AppDynamics for application performance, Splunk or ELK for logs and Prometheus for metrics. The good news? None of them is replaced by AIOps. It stretches them out.
