Artificial Intelligence for IT Operations information
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Machine learning term
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Artificial Intelligence for IT Operations (AIOps) is a term coined by Gartner in 2016 as an industry category for machine learning analytics technology that enhances IT operations analytics.[1] AIOps[2][3] is the acronym of "Artificial Intelligence Operations".[4][5][6] Such operation tasks include automation, performance monitoring and event correlations among others.[7][8]
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.[9]
The goal is to enable IT transformation,[10] 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.[11]
Given the inherent nature of IT operations, which is closely tied to cloud deployment and the management of distributed applications, AIOps has increasingly led to the coalescence of machine learning and cloud research.[12][13]
^Jerry Bowles (January 28, 2020). "AIOps and service assurance in the age of digital transformation". Diginomica.
^"Best practices for taking a hybrid approach to AIOps". 7 June 2021. Retrieved Nov 11, 2022.
^"Algorithmic IT Operations Drives Digital Business: Gartner - CXOtoday.com". Cxotoday.com. Archived from the original on January 28, 2018. Retrieved January 28, 2018.
^"Market Guide for AIOps Platforms". Gartner. Retrieved January 28, 2018.
^"Improve IT systems management productivity, application performance and operational resiliency with AIOps". IBM. Retrieved Nov 11, 2022.
^"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. 31 March 2017. Retrieved January 28, 2018.
^"AIOps: Managing the Second Law of IT Ops - DevOps.com". devops.com. 22 September 2017. Retrieved 24 January 2018.
^"What is AIOps or Artificial Intelligence for IT Operations. Top 10 Common AIOps Use Cases". Archived from the original on 2021-02-12.
^Harris, Richard. "Explaining what AIOps is and why it matters to developers". appdevelopermagazine.com. Retrieved 24 January 2018.
^Masood, Adnan; Hashmi, Adnan (2019), Masood, Adnan; Hashmi, Adnan (eds.), "AIOps: Predictive Analytics & Machine Learning in Operations", Cognitive Computing Recipes: Artificial Intelligence Solutions Using Microsoft Cognitive Services and TensorFlow, Apress, pp. 359–382, doi:10.1007/978-1-4842-4106-6_7, ISBN 978-1-4842-4106-6, S2CID 108316737
^Duc, Thang Le; Leiva, Rafael García; Casari, Paolo; Östberg, Per-Olov (September 2019). "Machine Learning Methods for Reliable Resource Provisioning in Edge-Cloud Computing: A Survey". ACM Comput. Surv. 52 (5): 94:1–94:39. doi:10.1145/3341145. hdl:11572/253114. ISSN 0360-0300.
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