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One approach is to convert the code to use parallelism. For an example of how to do it in Python using joblib see this article: http://blog.dominodatalab.com/simple-parallelization/

Even if you can't afford a 32-core instance, you might get to use 4 cores in your laptop.

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The problem's pretty obviously the Python implementation...Throwing more cores at it isn't really going to help.

Is it even easy to parallelise SVM training?

so the solution would be move from python to go? like python isn't that good for ML algorithms?

Python's fine if the heavy lifting is in a C extension. I use Cython for this; others prefer just numpy, maybe something like numba. But you can't just have a Python list of floats.

Java, C++, Scala and Julia are all popular choices. Go is probably fine too, although I know less about it.

Actually list support in numba is WIP

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