Among AI workloads,Trainingrequires themost computational power and data resources.

Why AI Training is Computationally Intensive?

Large datasets:

AI models (e.g., deep learning, neural networks)require millions or billions of labeled data points.

Training involvesprocessing massive amounts of structured/unstructured data.

High computational power:

Training deep learning modelsinvolves runningmultiple passes (epochs) over data, adjusting weights, and optimizing parameters.

Requiresspecialized hardwarelikeGPUs (Graphics Processing Units),TPUs (Tensor Processing Units), andHPC (High-Performance Computing).

Long training times:

AI model training can takedays, weeks, or even monthsdepending on complexity.

Cloud platforms offerdistributed computing (multi-GPU training, parallel processing, auto-scaling).

Cloud AI Training Benefits:

Cloud providers (AWS, Azure, GCP) offer ML training serviceswithon-demand scalable compute instances.

Supportsframeworks like TensorFlow, PyTorch, and Scikit-learn.

This aligns with:

CCSK v5 - Security Guidance v4.0, Domain 14 (Related Technologies - AI and ML Security)

Cloud AI Security Risks and AI Data Governance (CCM - AI Security Controls)​