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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 ].<ref>{{Cite web|url=https://diginomica.com/moogsoft-ceo-phil-tee-aiops-and-service-assurance-age-digital-transformation|title=AIOps and service assurance in the age of digital transformation|publisher=Diginomica|author=Jerry Bowles|date=January 28, 2020}}</ref> AIOps<ref name="CXO Today">{{cite web |url=http://www.cxotoday.com/story/algorithmic-it-operations-drives-digital-business-gartner/ |title=Algorithmic IT Operations Drives Digital Business: Gartner - CXOtoday.com |newspaper=Cxotoday.com |date= |author= |accessdate=January 28, 2018 |archive-url=https://web.archive.org/web/20180128074703/http://www.cxotoday.com/story/algorithmic-it-operations-drives-digital-business-gartner/ |archive-date=January 28, 2018 |url-status=dead }}</ref> is the acronym of "Algorithmic IT Operations".<ref name="Gartner">{{cite web |url=https://www.gartner.com/doc/3772124/market-guide-aiops-platforms |title=Market Guide for AIOps Platforms |website=] |date= |author= |accessdate= January 28, 2018}}</ref><ref name="Deloitte">{{cite web |url=https://www2.deloitte.com/content/dam/Deloitte/pl/Documents/Brochures/pl_en_Moogsoft_Deloitte_brochure.pdf |title= Comprehensive approach for Artificial Intelligence for IT Operations transformation|website=] |date= |author= |accessdate= January 28, 2018}}</ref><ref name="Tech">{{cite web |url=http://searchnetworking.techtarget.com/feature/ITOA-to-AIOps-The-next-generation-of-network-analytics |title=ITOA to AIOps: The next generation of network analytics |website=] |date= |author= |accessdate= January 28, 2018}}</ref> Such operation tasks include automation, performance monitoring and event correlations among others.<ref name="Register">{{cite web |url=https://whitepapers.theregister.co.uk/paper/view/6048/an-introduction-to-aiops |title=An Introduction to AIOps |newspaper=] |date= |author= |accessdate= January 28, 2018}}</ref><ref name="Dataconomy">{{cite web |url=http://dataconomy.com/2017/03/aiops-type-ai-nothing-artificial/ |title=AIOps - The Type of 'AI' with Nothing Artificial About It - Dataconomy |website=Dataconomy.com |date= |author= |accessdate= January 28, 2018}}</ref> | |||
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| There are two main aspects of an AIOps platform: ] and ]. In order to collect ] 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 ] is implemented against the combined IT data.<ref>{{cite web|url=https://devops.com/aiops-second-law-ops/|title=AIOps: Managing the Second Law of IT Ops - DevOps.com|date=22 September 2017|website=devops.com|accessdate=24 January 2018}}</ref> | |||
| The goal is to receive continuous insights which provide ] via automation. This is why AIOps can be viewed as ] for core IT functions.<ref>{{cite web|url=https://appdevelopermagazine.com/5047/2017/3/13/explaining-what-aiops-is-and-why-it-matters-to-developers/|title=Explaining what AIOps is and why it matters to developers|first=Richard|last=Harris|website=appdevelopermagazine.com|accessdate=24 January 2018}}</ref><ref>{{cite web|url=https://squadex.com/insights/aiops-101/|title=AIOps 101|date=5 March 2018|publisher=}}</ref> | |||
| Given the inherent nature of IT operations being closely tied to cloud deployment and the management of distributed applications, AIOps has increasingly led to the coalescence of ] and ].<ref>{{Citation|last=Masood|first=Adnan|title=AIOps: Predictive Analytics & Machine Learning in Operations|date=2019|work=Cognitive Computing Recipes: Artificial Intelligence Solutions Using Microsoft Cognitive Services and TensorFlow|pages=359–382|editor-last=Masood|editor-first=Adnan|publisher=Apress|language=en|doi=10.1007/978-1-4842-4106-6_7|isbn=978-1-4842-4106-6|last2=Hashmi|first2=Adnan|editor2-last=Hashmi|editor2-first=Adnan}}</ref><ref>{{Cite journal|last=Duc|first=Thang Le|last2=Leiva|first2=Rafael García|last3=Casari|first3=Paolo|last4=Östberg|first4=Per-Olov|date=September 2019|title=Machine Learning Methods for Reliable Resource Provisioning in Edge-Cloud Computing: A Survey|journal=ACM Comput. Surv.|volume=52|issue=5|pages=94:1–94:39|doi=10.1145/3341145|issn=0360-0300|url=https://zenodo.org/record/3471441|doi-access=free}}</ref> | |||
| Please note that this is different from ], which uses ] ideas for ]. | |||
| ==References== | |||
| {{reflist}} | |||
| ==External links== | |||
| * Open AIOps Platform: '''' | |||
| * . Search IT Operations via '''TechTarget''' | |||
| ] | |||
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