Clearly we're talking decades, but I am already realizing pay-offs from the switch to Julia (from Python) in my domain.
Rackauckas pointed out an amazing example recently [1]:
https://discourse.julialang.org/t/differentialequations-jl-a...
You have a library that implements calculating with numbers with uncertainties. You have the differential equations library. They don't know about each other but you can use the former in the latter to solve differential equations with uncertainties with a whole bunch of advanced solving algorithms.
This sort of power, having an ecosystem like Python that combines in a performant way, will be huge.
[1] http://www.stochasticlifestyle.com/why-numba-and-cython-are-...
- scikitlearn :: no single package since skl is a meta-package of sorts, but most of the stuff is there spread across the eco-system
- Keras :: checkout Flux or Mocha
Welcome to Julia! Also take a look here [1] for more package that might be to your interest.
[1] https://github.com/svaksha/Julia.jl/blob/e305195ab60e6859e78...
Keras also many. TensorFlow.jl, Flux, Mocha, KNet, MxNet.
Sklearn is all there but bring it together will take a bit. Some important parts are in JuliaML org. Also Clustering.jl, and MultiariateStats.jl (For DR) the classifiers are really scattered. I'ld love to fix that if I had time.