Jug is a generic framework, but it's ideally suited for medium-scale data analysis. As you develop your analysis pipeline, it's good to have intermediate results automatically saved. If you have already computed the preprocessing step before and are only changing the features you compute, you do not want to recompute the preprocessing step. If you have already computed the features but want to try combining a few new ones into the mix, you also do not want to recompute all your other features.
Jug is also specifically optimized to work with NumPy arrays. Whenever your tasks return or receive NumPy arrays, you are taking advantage of this optimization. Jug is another piece of this ecosystem where everything works together.
We will now look back at Chapter 12, Computer Vision. In that chapter, we learned how to compute features on images. Remember...
