I used Julia in a robotics project doing statistics/estimation/easy optimization but not deep learning. I also do ML/DL: Julia vs. Python - PyTorch is standar

I used Julia in a robotics project doing statistics/estimation/easy optimization but not deep learning. I also do ML/DL: Julia vs. Python - PyTorch is standard and it is hard to convince other people to switch - long compile time on startup during deployment (not so good for a robot) but also for plotting; other people really hated this Julia vs. C++ - Julia has a JIT and is MUCH faster than Python if you cannot write it as a sequence of numpy operations, e.g. if you have loops and if-blocks in the main loop; C++ obviously also shines here - however, similar to Python you can only detect problems of the code when running it - the linters etc. are not good enough; hence, I also fear changing only a few lines; programming in C++ is much easier and you have much more confidence that the code is correct if it compiles After learning JAX in Python, which compiles numeric code JIT, I have almost no reason using Julia anymore. Of course, DifferentialEquations.jl and many optimization libraries are top notch.