+1
Default keras pipeline is fast, beginner friendly and great. As soon as something custom needs to be done - it's just painful if even possible.

Also the last time I checked (~a year ago) some features were heavily bugged ever since the introduction (literally for years). Like model.predict or tf.function cause memory leaks even with the "examples" code. That was the switching point for me.

There isn't much difference between "Simply 👍/👎" and "scale of 1-5". They will probably give ~same results. I understand first one as {0, 1} and second as {0, ... , 1}. It's just the question of resolution. the 1-5 thing will most likely give you faster convergence, but it can also f you up if some of your data gets mislabeled. Since it's easier to make mistakes with high resolution.

But in a limit, if you take 1 million different people and ask them to asses your model in a binary fashion, or on a scale of 1 to 10, and then average out results, you will get the same thing. It's just from a human perspective, it's easier to asses things as yes-no. (e.g., "did you like this new movie?" vs "how would you rate this movie on a scale from 1 to 10?"). But from computer's perspective, ML wants that label to be as close to its true value as possible.