Julia lacks ecosystem outside of academic stuff.

It's quite complete for anything numerical/machine learning/statistical at this point, with almost 2000 packages. This is in addition to the very comprehensive standard library, which includes everything you get in Numpy/Scipy. Of course, outside of numerics, you're pretty much out of luck, which is not the case for Python.

It also has multiple dispatch like Common Lisp, as well as gradual typing. All functions are multimethods, actually. This is an enormous abstraction advantage over Python. It's much easier to do complicated things without pulling your hair out in comparison to Python.

I use it a fair amount in my job as a data scientist. It's also what I reach for if I need to write some custom algorithm myself that needs to be high performance, rather than doing it in C.

Does it have a tooling for deployment? Is there a good way to deploy a machine learning model in production?

I don't find Julia to be nearly as programmer-friendly as Python, with more syntax and more cognitive overhead. This is on an admittedly small experience base.

This. If you need to drop out of regular Python for performance reasons, then Julia offers the same high level flexibility, but with types and performance.

Cython basically lets you sprinkle types and performance into your Python code, as needed. It's not as drastic a departure as switching languages.

There's a reason most high-performance Python libraries are not written that way, and core routines are just written in C instead. Proof of the pudding is in the eating!

See this talk by Armin Ronacher (creator of Flask) on why the design of python makes it fundamentally unfriendly to performance optimizations: https://youtu.be/qCGofLIzX6g?t=171

Julia has be designed ground up to avoid several such problems. See this discussion: https://discourse.julialang.org/t/julia-motivation-why-weren...

If your domain falls under the umbrella of numerical and scientific computing, writing Julia is as painless as writing python, with code that automatically runs roughly as fast as C. If you're used to writing numpy, you can hit the ground running in Julia, with maybe a few hours to become comfortable with the slightly different syntax and the names of useful libraries.

> There's a reason most high-performance Python libraries are not written [with Cython], and core routines are just written in C instead.

Pandas, Scipy and lxml are large, very popular Python libraries that use Cython. The article even mentions them at the end.

The point is that Cython provides a nice intermediate stage between C and CPython. Most optimizations need the first factor of 100, not the last factor of 2. You can usually achieve that in Cython with an effort measured in characters changed rather than lines of code changed.

I've played with Julia. It's nice enough, but it doesn't offer me anything I don't already get through the C/Cython/CPython hierarchy.

It offers a ton that you don't get from Cython: http://www.stochasticlifestyle.com/why-numba-and-cython-are-...