Why Julia is beneficial for Machine Learning?

Original article can be found here (source): Artificial Intelligence on Medium

Perhaps some years from now Python hats won’t be so impeccably sewed on us. Maybe that space will be taken up by Julia. Or who can say, both may co-exist (read the part on consensus below).

While Python is well established and is celebrated as the №1 programming language, Julia is proving as incredible and unique for ML professionals working in predictive modeling, statistics, visualization, machine learning.

Without a doubt, Julia is powering the analytical tools at many high-profile organizations. The biggest corporations of the world, including Amazon, Apple, Disney, Facebook, Google, IBM, Microsoft, Oracle, NASA, are using Julia and hiring ML specialists with its know-how. Then, why it’s not as mainstream? Possibly, it needs more loud shootouts.

Julia is good because…

As a data scientist and machine learning expert, learning all languages is fun. Julia, in particular, is a dynamic language to acquire and utilize for many reasons. It is interesting to some because of its oddities (unlike Python, it’s not object-oriented). For others, its unique features make it a can’t-miss.

Learning Julia to program AI applications is productive as:

  • It is best armed to address Deep Learning (after Python).
  • It was specifically designed for quickly implementing basic mathematics and scientific queries.
  • Everything in Julia has been built for ML professionals — such that it is centered on high-performance numerical analysis.

Here’s more on how this open-source programming language developed by MIT, can be a turnaround for Machine Learning.

Julia for Machine Learning

👉Bridge Between Python and Spark

With the machine learning base of Python, Julia is simple like Python, and at the same time has big-data manipulation powers of Spark. For data scientists beginning their careers, this feature of Julia is something that must be taken note of.

Its syntax is similar to that of MATLAB and Microsoft’s Basic (that’s right), which ensures that Julia is easier to transition to.


Designed with ML in mind, Julia focusses on the scientific computing domain and is highly compatible with it. It is well suited for tasks involving intensive computation. Julia provides execution speeds analogous to C and C++, with an add on parallelism aspect built into its model.

From milk production optimization at dairy farms to supercomputing in astronomy, the language has come to be used in a wide-ranging base of applications. A common theme in all of Julia’s applications is they all require scientific and sometimes parallel computation.

👉Scalability (a bit of limitation there)

Due to its computing abilities, Julia is scalable and is faster than Python and R. If one is working on big data in a distributed system, it can be deployed for large clusters swiftly.

For machine learning and artificial intelligence, Julia offers incredibly powerful native tools such as MLBase.jl, Flux.jl (perfect to write auto diff code) and others. As an add-on tool for building ML systems with Julia, it has SckikitLearn.jl, though it’s not an official port. To top it all, Julia also has TensorFlow.jl and MXNet.jl. An ML specialist who is already familiar with these tools can easily transition to Julia.

That said, Python (for now) triumphs in the aspect of scalability with many fantastic libraries. It is primarily because Python is object-oriented (you can develop web applications with Flask, Django that work with SQL databases). Comparatively, Julia lacks there. It is yet to be seen how Julia takes over Python in this respect.

The Consensus.

Compared to languages that have been around for over 15 years, Julia is just coming out of its 1.0 version. It is still in its infancy. Owing to the fact that Python is older than Julia, it works in the former’s advantage. However, many experts are enjoying the best of both worlds.

For some ML professionals, Julia makes for a good choice for writing deep learning code at the backend (currently dominated by C++) since it is where performance matters the most, and Python is utilized at the frontend where ease of use matters.

Think of the slow times when Python and C++ were new kids in the town. Their uptake was sluggish. Julia’s language ecosystem — predictive models and statistical libraries — is being actively developed by MIT and other developers around the world. Being mindful of its computing abilities, Julia can become a widely adopted, standardized language tool in Data Science and machine learning.