Artificial Intelligence for IT Operations

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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] is the acronym of "Algorithmic IT Operations".[3][4][5] Such operation tasks include automation, performance monitoring and event correlations among others.[6][7]

There are two main aspects of an AIOps[8] 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 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.[10][11]

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 machine learning and cloud research.[12][13]

This is different from MLOps, which uses DevOps ideas for machine learning to bridge the gap between ML model building and their execution.[14]


  1. ^ Jerry Bowles (January 28, 2020). "AIOps and service assurance in the age of digital transformation". Diginomica.
  2. ^ "Algorithmic IT Operations Drives Digital Business: Gartner -". Archived from the original on January 28, 2018. Retrieved January 28, 2018.
  3. ^ "Market Guide for AIOps Platforms". Gartner. Retrieved January 28, 2018.
  4. ^ "Comprehensive approach for Artificial Intelligence for IT Operations transformation" (PDF). Deloitte. Retrieved January 28, 2018.
  5. ^ "ITOA to AIOps: The next generation of network analytics". TechTarget. Retrieved January 28, 2018.
  6. ^ "An Introduction to AIOps". The Register. Retrieved January 28, 2018.
  7. ^ "AIOps - The Type of 'AI' with Nothing Artificial About It - Dataconomy". Retrieved January 28, 2018.
  8. ^ "Artificial Intelligence for IT operations(AIOps)". Algomox. Algomox. Retrieved 25 November 2020.
  9. ^ "AIOps: Managing the Second Law of IT Ops -". 22 September 2017. Retrieved 24 January 2018.
  10. ^ Harris, Richard. "Explaining what AIOps is and why it matters to developers". Retrieved 24 January 2018.
  11. ^ "AIOps 101". 5 March 2018.
  12. ^ 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
  13. ^ 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. ISSN 0360-0300.
  14. ^ "MLOps vs AIOps". Retrieved 2020-10-07.

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