MXNet: A Language-agnostic Paradigm for Fringe Data Science

I have talked in another post about the new kind of data science that is becoming more and more popular nowadays. Namely, there is a kind of data science that leverages A.I. via a framework known as Deep Learning. This is what I refer to as fringe data science, since it is without a doubt the state-of-the-art of the field. However, even though it’s so advanced that I may not be able to describe in a blog post, it is not without its issues. Namely, up until now it’s been limited by the languages involved in its implementation. So, if you want to use Theano, for example, you need to do so in Python. And although Python is a lovely and very versatile tool, it may not be your forte. So, what do you do? Well, now it seems that there is a system for ML and DL that doesn’t care about which language you use. This system, developed by Amazon, is called MXnet (pronounced: mix-net).
 
MXNet is not yet another system for deep learning or machine learning in general. It is a paradigm-shift kind of tech. What’s more, it embraces a number of different programming languages, such as C++, Python, R, Go, and Julia. In other words, you don’t need to be a developer to work it. Even us high-level coders who use programming to tackle data science tasks can make use of it. This is huge. With this system you can have a team of diverse professionals who can collaborate on projects via this platform. You don’t need to make your company a Python shop, or an R shop, for example. Also, if you have some data scientists in your company who are more fox-like and like to experiment with new programming technologies, such as Julia and Go (not to be confused with the popular strategy game), there is a place for them too!
 
So, what do you think? Is this new tech worth all the hype that Amazon scientists bring about with their articles? Is it a hype that some tech journalists have created to make money off their articles? Or is it an actually useful tech? Feel free to let me know in the comments below.
 
References

1. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems, by Tianqi Chen (University of Washington) and many others (link)
   
2. Geekwire article
3. Github repository
4. Official website