Using AIOps to Reduce Incident Volumes
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03.06.19 – 1 29848:35Популярные
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Опубликовано 3 июня 2019, 17:27
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Customers migrating workloads to AWS have a variety of tools to monitor their infrastructure, generating large volumes of alarms from services such as Amazon CloudWatch, AWS Config, and other third party tools. Without careful curation, events and tickets can exponentially multiply and overwhelm ITSM systems and the teams operating them, obscuring real problems and wasting time. Using advanced Machine Learning techniques, customers can reduce noise from these events and tickets and increase their service quality. In this presentation, we explore challengs of adopting AIOps, and provide examples of how AIOPs can be used to reduce Mean Time To Restore and improve customer outcomes.
Customers migrating workloads to AWS have a variety of tools to monitor their infrastructure, generating large volumes of alarms from services such as Amazon CloudWatch, AWS Config, and other third party tools. Without careful curation, events and tickets can exponentially multiply and overwhelm ITSM systems and the teams operating them, obscuring real problems and wasting time. Using advanced Machine Learning techniques, customers can reduce noise from these events and tickets and increase their service quality. In this presentation, we explore challengs of adopting AIOps, and provide examples of how AIOPs can be used to reduce Mean Time To Restore and improve customer outcomes.
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