If you're really happy with your current tools, Julia's probably not for you right now, and that's totally fine. Julia's users tend to have the opposite of the

If you're really happy with your current tools, Julia's probably not for you right now, and that's totally fine. Julia's users tend to have the opposite of the "established libraries + glue" use case; something more like "custom data structures + unvectorisable numerics". It's better for the people who are writing the next scikit-learn than for those who are using it. The main advantage of Julia's JIT is that the performance of a given piece of code is easy to predict and debug – this is a classic problem with tracing JITs.