AI: AI-ML fundamentals & governance

AI-ML workloads and their infrastructure impact

In one line Different AI workloads - and above all training vs inference - stress infrastructure in very different ways, and that profile is what drives every fabric, compute, and storage decision in this elective. The workload spectrum Classic ML (often CPU, lighter) -> deep learning (GPU) -> LLM / GenAI (massive GPU

Service placement - on-prem, cloud, hybrid, distributed

Requirement / business driver Decide where AI workloads run, balancing data gravity, cost/ROI, GPU availability, latency, and compliance. Functional vs non-functional requirements Placement decisions hang on two requirement types. Functional requirements define what the system does - scalability, integration with existing infrastructure/services, user experience, security capability. Non-functional requirements

AI: AI-ML fundamentals & governance

Data sovereignty and data gravity

In one line Data-residency law and the sheer mass of training data ("data gravity") frequently dictate where AI infrastructure must sit - often overriding cost or elasticity preferences. Data sovereignty Residency / localization rules require data to stay in-country or in-region; cross-border transfer is restricted; sovereign-

AI: AI-ML fundamentals & governance

Regulations, governance, and AI security policy

Requirement / business driver Meet regulatory and governance obligations and manage the new corporate-security-policy risks that AI introduces. AI governance Model and data governance - lineage, versioning, auditability; assurance and integrity (the model behaves as intended; training/inference data is trustworthy and unaltered); and access control to models and datasets.

AI: AI-ML fundamentals & governance

Cost, ROI, and scalability of AI infrastructure

In one line GPUs are extraordinarily expensive, so cost/ROI and utilization dominate AI-infra decisions - and training vs inference scale very differently. Cost CapEx (own GPUs + power + cooling) vs OpEx (cloud rental). GPUs dominate TCO, and power/cooling (Sustainability - Green AI, power and cooling) is a major ongoing cost

AI: AI-ML fundamentals & governance

Workload placement, mobility, and vendor lock-in

Where an AI workload runs - and how freely it can move - is a design and business decision, not just an ops one. This note covers mobility (moving workloads across DC/cloud/edge) and interoperability / vendor lock-in (keeping that freedom). It builds on the static placement trade in Service placement