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)
