Cloud-Based Infrastructure for Artificial Intelligence and Machine Learning Workloads: A Scalable Framework for Saudi Enterprises

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@article{1723949,
      author = {Mohammed Vaquar Ahmed Haseeb},
      title = {Cloud-Based Infrastructure for Artificial Intelligence and Machine Learning Workloads: A Scalable Framework for Saudi Enterprises},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {1641-1655},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723949.pdf},
      abstract = {In Saudi companies artificial intelligence and machine learning are moving from being mere experiments to becoming operational capabilities, which is leading to an infrastructure issue that goes well beyond the need for computing power. The processes involved in training, fine-tuning, inference, feature preparation, model monitoring, governance and retraining all have diverse requirements for accelerators, storage, networking, data locality, elasticity and reliability. This review draws on research carried out between 2020 and 2025 in order to look at how cloud architecture can support these types of workloads on an enterprise level while taking into account the Saudi situation regarding rapid digital transformation, the adoption of cloud services, data governance and the modernisation of various sectors. The review applies a structured evidence-synthesis approach focused on infrastructure architecture, workload orchestration, MLOps, cloud-edge distribution, security, reliability, cost control and organisational readiness. The evidence shows that scalable AI infrastructure should be viewed as a coordinated platform rather than as a set of separate services; effective designs incorporate elastic pools of CPU and GPU resources, container orchestration, tiered data services, automated model pipelines, observability, policy controls, workload-aware scheduling and selective edge execution. The Saudi Enterprise AI Cloud Framework proposed in this review combines these capabilities into six layers and includes a closed operational loop that links demand prediction, placement, execution, telemetry, policy evaluation and lifecycle learning. The review maintains that scalability must be assessed together with availability, cost predictability, data sovereignty, operational resilience and discipline in managing the model lifecycle. It ends with an implementation roadmap and a research agenda for businesses that wish to industrialise AI as part of their digital transformation aligned with Vision 2030.},
      month = {October},
  }