AIOps stands for and is the acronym of Algorithmic IT Operations, synonymously titled as Artificial Intelligence for IT Operations. Such operation tasks include automation, performance monitoring and event correlations among others.
AIOps platform structure
There are two main aspects of an AIOps platform: Machine learning and big data. In order to collect observational data and engagement data that can be found inside a big data platform and requires a shift away from sectionally segregated IT data, a holistic machine learning and analytics strategy is implemented against the combined IT data.
The goal is to receive continuous insights which provide continuous fixes and improvements via automation. This is why AIOps can be viewed as CI/CD for core IT functions.
- IT operations analytics
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- "Market Guide for AIOps Platforms". Gartner. Retrieved January 28, 2018.
- "Comprehensive approach for Artificial Intelligence for IT Operations transformation" (PDF). Deloitte. Retrieved January 28, 2018.
- "ITOA to AIOps: The next generation of network analytics". TechTarget. Retrieved January 28, 2018.
- "An Introduction to AIOps". The Register. Retrieved January 28, 2018.
- "AIOps - The Type of 'AI' with Nothing Artificial About It - Dataconomy". Dataconomy.com. Retrieved January 28, 2018.
- "AIOps: Managing the Second Law of IT Ops - DevOps.com". devops.com. 22 September 2017. Retrieved 24 January 2018.
- Harris, Richard. "Explaining what AIOps is and why it matters to developers". appdevelopermagazine.com. Retrieved 24 January 2018.
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