Feature stores play a crucial role in streamlining the model training process by caching preprocessed data, which can significantly enhance training speed. By normalizing and preparing datasets ahead of time, developers can avoid redundant computations, allowing for more efficient experimentation with different training methods. However, in scenarios like computer vision, where data augmentation is necessary, the approach to caching can vary, highlighting the balance between efficiency and flexibility in data handling.
