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Which type of AI workload typically requires large data sets and substantial computing resources?
Among AI workloads, Training requires the most 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 involves processing massive amounts of structured/unstructured data.
High computational power:
Training deep learning models involves running multiple passes (epochs) over data, adjusting weights, and optimizing parameters.
Requires specialized hardware like GPUs (Graphics Processing Units), TPUs (Tensor Processing Units), and HPC (High-Performance Computing).
Long training times:
AI model training can take days, weeks, or even months depending on complexity.
Cloud platforms offer distributed computing (multi-GPU training, parallel processing, auto-scaling).
Cloud AI Training Benefits:
Cloud providers (AWS, Azure, GCP) offer ML training services with on-demand scalable compute instances.
Supports frameworks 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)