According to MLOPs principle, it is recommended to have a feature store. The question is in the context of doing image classification using deep learning models like convolutional neural networks which does automatic feature engineering(using convolution layers) as part of the training process.
- Does it make sense to have a feature store for pure image classification/segmentation models?
- What features should be stored in the feature store? Output of convolution layers? but then they cannot be reused during the training since during training these will be rebuild by the convolution layers.