Part 3/5:
One of the critical challenges in training these models is the high rate of iteration. During development, if a bug appears in the training code or if the training process doesn't converge properly, all the invested resources—both time and money—can be lost. Since training large models involves multiple trial-and-error cycles, this risk is a significant concern for researchers and organizations alike.
The End-to-End Approach
The prevalent approach in recent years has been the “end-to-end” architecture, where a single pipeline takes raw input data and produces the final output without extensive intermediate adjustments. While this simplifies the overall system, it introduces difficulties in fine-tuning the model's behavior.