Abstract
The operations landscape today is more complex than ever. IT Ops teams have to fight an uphill battle managing the massive amounts of data that is being generated by modern IT systems. They are expected to handle more incidents than ever before with shorter service-level agreements (SLAs), respond to these incidents more quickly, and improve on key metrics, such as mean time to detect (MTTD), mean time to failure (MTTF), mean time between failures (MTBF), and mean time to repair (MTTR). This is not because of lack of tools. Digital enterprise journal research suggests that 41 percent of enterprises use ten or more tools for IT performance monitoring, and downtime can get expensive when companies lose a whopping $5.6 million per outage and MTTR averages 4.2 hours and wastes precious resources. With a hybrid multi-cloud, multi-tenant environment, organizations need even more tools to manage the multiple facets of capacity planning, resource utilization, storage management, anomaly detection, and threat detection and analysis, to name a few.
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© 2019 Adnan Masood, Adnan Hashmi
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Masood, A., Hashmi, A. (2019). AIOps: Predictive Analytics & Machine Learning in Operations. In: Cognitive Computing Recipes. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-4106-6_7
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DOI: https://doi.org/10.1007/978-1-4842-4106-6_7
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Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-4105-9
Online ISBN: 978-1-4842-4106-6
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