SVMs train very quickly, perhaps since their decision boundaries are more likely to be simple than for most other algorithms. While this should not be a problem in your notebooks solving small-scale Data Science and Machine Learning problems, large applications are also glad to see that SVMs preserve memory space on Computers.
On the other hand, SVMs can be prone to overfitting the data, since they are able to visualize the data in its complicated glory. This means that the SVM’s result becomes hard to generalize. For large data sets, they also take up a lot of computing power.