Model training in AI is the process of feeding large datasets into an algorithm, allowing it to learn from data patterns and improve its predictive accuracy over time. During training, the model adjusts its internal parameters through iterative learning, using techniques like supervised, unsupervised, or reinforcement learning to refine its outputs. This phase is computationally intensive and requires extensive data storage and rapid access to handle large-scale datasets effectively. VDURA’s storage systems are optimized to support the high-speed access and substantial data volumes necessary for model training, enabling efficient data processing and reducing training time. By providing robust storage infrastructure, VDURA ensures AI models are trained accurately and quickly, ready for deployment in data-driven applications.