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

This research review looks at the architectural needs for moving artificial‑intelligence and machine‑learning workloads from experimental stages to full‑scale, enterprise‑level operation, with a focus on Saudi Arabia’s digital‑transformation efforts. It gathers evidence from 2020‑2025 and tackles infrastructure challenges that go beyond raw compute power, such as training, fine‑tuning, inference, feature preparation, model monitoring, and data governance. The paper proposes a coordinated platform rather than a collection of isolated services. It stresses the importance of elastic CPU and GPU resource pools, container orchestration, tiered data services, and automated MLOps pipelines. It also highlights the need to balance cloud‑edge distribution, security, reliability, and cost while complying with local data‑residency regulations. For B.Tech

students, the review offers a structural blueprint for designing and simulating scalable, cloud‑native ML pipelines. The main limitation is that the work is a high‑level architectural synthesis and literature review, not an empirical benchmark of any single proprietary cloud provider. Students will therefore need to adapt these concepts to specific environments such as AWS, Azure, or local sovereign clouds.

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