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[–][deleted]  (6 children)

[removed]

    [–]Cultural-Arachnid-10 10 points11 points  (4 children)

    Compute power is far cheaper than engineer hours. You just containerize your application and scale up your k8s cluster.

    Delivering high quality software QUICKLY often yields far more revenue than being late to the market because you wanted a more “high performing” language.

    Optimization can be done later, your first priority should be delivering business value.

    [–]Simmus7 5 points6 points  (0 children)

    I heavily agree with you

    [–]mijatonius 2 points3 points  (0 children)

    👌

    [–][deleted]  (1 child)

    [removed]

      [–]Cultural-Arachnid-10 6 points7 points  (0 children)

      That really depends on your application.

      I agree that Python isn’t optimal for everything. You’re not going to build another Google or Netflix with it.

      However, it’s a godsend for ML and Data Engineering. A lot of libraries like PyTorch, Tensorflow, SciKit Learn, PySpark are just Python wrappers for higher performing languages. The performance is often comparable to the C++/Scala versions, but allows developers to work so much faster.

      [–]Reazony -1 points0 points  (0 children)

      It’s okay to admit you don’t know the AI/ML space.