Should I use keras or sklearn for PCA? Recentl I saw that there is some basic overlapping of functionality between keras and sklearn regarding data preprocessing.
So I am a bit confused that whether should I introduce a dependency on another library like sklearn for basic data preprocessing or should I stick with only keras as I am using keras for building my models.
I would like to know the difference for scenarios like

which is good for production
which will give me better and faster response
is there any issue with introducing dependency over other libraries for just 1 or 2 functionality
which has a better compatibility with other tools like tensorboard or libraries like matplotlib, seaborn etc.

Thanks in advance.
What I would suggest is to build a sklearn pipeline in which one step will be the sklearn PCA and the last step will be your Keras model.
Sklearn pipelines are easy to put into production and can handle a lot more of transformations.