As the complexity and energy demands of AI models continue to rise, it's evident that the training or learning must occur in the core, while inferencing and fine-tuning can take place at the edge. To achieve scalable AI adoption, an end-to-end solution is essential—one that harnesses the cloud for learning, enables inferencing at the edge, and leverages industry-specific insights to craft compact, energy-efficient models.