-2
All these three seems to be very closely related and are used post model is created and trained.

Explainable AI :- Shows why model has given the prediction that was in the output. It shows you which part of image was more prominent to make a decision or which feature in text is more prominent.
TensorBoard :- provide a holistic view on metrics, loss, fairness, hyperparameter tuning, model performance on machine
What-If Tool:- This combines both explainable AI and TensorBoard techniques but only for text classifications and text regression models.

It is very difficult to judge when to use which tool especially for beginners , any help in this regard? Any easy cheat sheet which can clearly tell the difference and hence help me understand when to use what.