The discussion highlights the importance of optimization in both training and inference, emphasizing that the specific use case dictates the urgency and frequency of model training. For instance, real-time training is crucial for news platforms, while fraud detection models may only need daily updates. Additionally, fine-tuning pre-trained models can enhance performance, even if it requires distributed training across multiple machines due to their size.
