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To add to that, when you're using Python for ML (or for anything intensive really) you're most likely making calls to very efficient implementations in C\C++.
Concurrency was never one of Python's strongest suits anyway and it's not why it's preferred for anything.
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Though I am not a machine learning engineer, I have heard that Python is loved for ML because of three main reasons:
libraries/frameworks (like NumPy for computation, Pandas, PyTorch, scikit-learn for data mining, and more)
the simplicity of the code is useful when dealing with and testing complex algorithms
there's a big community of support using Python for ML, so it's easy to get advice and collaborate
To add to that, when you're using Python for ML (or for anything intensive really) you're most likely making calls to very efficient implementations in C\C++.
Concurrency was never one of Python's strongest suits anyway and it's not why it's preferred for anything.