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)