Machine Learning: Essential in Client-Centric Cloud Service Management


As businesses today compete more and more on digital experience, you need to ensure that what’s delivered performs optimally and provides an outstanding customer experience. With increasing complexities, growth and change in your IT infrastructure, you need complete visibility into the performance and availability of IT infrastructure, and also the applications that ride on it.  To succeed in this modern domain, your IT operations team needs to be more proactive than reactive.

Proactive operations management have long been a goal of client-centric operations: the ability to avoid issues rather than simply reacting to issues more effectively or more efficiently. It is time traditional IT service management transforms to remain relevant. The good news is that machine learning has advanced to a stage where this is achievable at scale.

Machine learning may have been a distant dream, but not anymore. Almost every system or gadget, whether complex or simple, is getting smarter using basic pattern recognition and computational learning, which is the basis of any machine learning technology. Today, public cloud service providers offer machine learning services that make this technology affordable. So, you are not too far away from making your IT operations management more client-centric.

Machine Learning Meets IT Service Management

Applying cognitive and machine learning capabilities to service management will help accelerate diagnosis of events and patterns, extract deep insights from IT systems and provide early warnings of anomalies that could cause service impact or poor performance.

Machine learning will enable your IT service management to:

Continuously learn: Cognitive service management uses machine learning to learn the behavior of applications and resources and get a true understanding of how it should function normally. With machine learning, you can continuously learn application and infrastructure behavior and then use those insights to set and dynamically manage thresholds for all monitoring data. You can understand the relationships across applications and resources to anticipate service impacts. With these deeper insights, you can quickly and efficiently resolve issues, improve overall operational efficiency and significantly reducing operational costs.

Anticipate and adjust: With every outage is a potential service degradation. This is exactly where machine learning capabilities help proactively detect and avoid, which traditional service management capabilities lack. Insights from recurring anomalies help forecast potential service degradations. This information is used to proactively alert on potential problems so that organizations can adjust to the rapidly changing environments and intelligently prioritize their resolution.

Recommend action: Efficiency is critical when it comes to finding and fixing application and systems problems. A skilled service organization backed with machine learning capabilities helps provide expert advice for taking corrective action and can offer greater service assurance. Applying cognitive capabilities turn terabytes of data captured from their IT infrastructure, into relevant and actionable insights for quicker problem solving and better service.

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