Research Article Open Access

Artificial Intelligence Operating Model: A Proposal Framework for AI Operationalization and Deployment

Mustapha Lahlali1, Naoual Berbiche1 and Jamila El Alami1
  • 1 Laboratory of Systems Analysis, Information Processing and Industrial Management, Higher School of Technology, Mohammed V University, Rabat, Morocco

Abstract

At the heart of the newenterprise, across all activities, is a decision factory governed by some kindof intelligence. Among the great promises of Artificial Intelligence (AI) isits ability to lead to a significant evolution in the amount of data received,processed, or generates by companies, particularly those with a digitalconnotation. To bring about dramatic changes, AI does not need to be sciencefiction but simply a new way of approaching computerization subjects whether interms of design, development, or terms of expected results. It should be notedthat traditional IT solutions present a form of AI called - Weak AI - while theAI that is the subject of much noise, hype, and promises of transformation andpotential for growth is called - Strong AI -. This article aims to present, ina didactic way, a model called D2MO (For Data Ops, ML Ops, Model Ops, and AIOps) allowing the company to operationalize, in a structured approach, AIsubjects, activities, and projects. We target through this article to provideboth IT and business experts with a new framework offering a perfectarticulation between the different bricks and actors entering into thecomposition of an AI-based system thus allowing them to operate in harmony andan agile mode while taking advantage of this technology.

Journal of Computer Science
Volume 18 No. 11, 2022, 1100-1109

DOI: https://doi.org/10.3844/jcssp.2022.1100.1109

Submitted On: 5 July 2022 Published On: 16 November 2022

How to Cite: Lahlali, M., Berbiche, N. & El Alami, J. (2022). Artificial Intelligence Operating Model: A Proposal Framework for AI Operationalization and Deployment. Journal of Computer Science, 18(11), 1100-1109. https://doi.org/10.3844/jcssp.2022.1100.1109

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Keywords

  • Artificial Intelligence
  • ML Ops
  • AI Ops
  • Data Ops
  • Model Ops