These days, AI programming has gone far beyond pure software or hardware development firms and also expanded. Companies within most demanded verticals, like e-commerce, real estate, healthcare, and more, start adopting AI in their systems.
Python and Golang are the most popular programming languages for AI used in the companies. As a company that has worked with both—we know it can be difficult to choose the right one for a particular program.
Recently we have seen that Golang web programming capabilities comparing
it to other languages. Now, it’s time to see which language, Golang or Python,
is better specifically for AI programming.
What
Python brings to AI programming
You can hardly consider any programming language perfect, but certainly,
Python has its strengths in the context of AI. Here are the most significant
ones:
Well
established community
The community and the ecosystem around Python are vibrant and active. Community
contributes much to creating new libraries, updating documentation, and
extending toolset.
Language
accessibility
Python is an accessible programming language, and it keeps gaining more
ground. For businesses, accessibility means a vast market of Python experts.
Python’s
downsides
However, while Python is sometimes referred to as the best programming
language for AI, it has its disadvantages.
Bad for
large-scale engineering
When it comes to work involving a few hundred programmers, Python
clearly losing to Golang scalability.
It’s also challenging to use Python if you require a very ordered and disciplined
way to do programming. The same is true when you are going to deploy very
complex AI systems.
Codebase
may be difficult to maintain
Python offers many libraries, supports multiple systems, and third-party
integrations. But, such variety often plays against Python.
From many projections, developers state that Python is not easy to
maintain. How so? Python lacks several language features like static type
system. Also, its syntax is confusing and goes against assumptions that other
programming languages make. Plus, libraries related to different versions of
Python often conflicts with each other. Such conflicts cause problems with
configuring a specific cluster or even leads to a general stop of working code.
Lack of
performance and multicore processing
Another challenge with Python is its performance, specifically CPU and
GPU processing. There are ways to get around this challenge, but they are
mostly tweaks. What works for specific use cases often just can’t be applied
for most common uses.
Too many
versions of Python available
Even the most dedicated Python programmers find this point painful. The
transition between versions and disconnection between Python 2 and Python 3 are
just several issues. Also, simultaneously having several versions can require
installing different environments. When you need them ready to work
immediately, it can cause a mess and technical problems.
One more challenge that adds developers confusion is packaging systems.
Packaging systems in different versions are broken down in different ways which
is hard to manage and document. In turn, different packaging systems may
require installing multiple environments.
Golang
advantages for AI programming
Does Golang have what it takes to beat Python? Let’s take a look
at Golang advantages.
Libraries are written in Go comfortable for Go developers
Golang developers don’t need to call out to libraries written in other
languages. Programming a purely Go solution means having fewer pieces from different
languages. But the main advantage of having these libraries in Go isn’t
deployment, but developer comfort.
Covering
vast AI purposes
The number of libraries Golang offers is small (but consistently
growing) and addresses a wide range of purposes. Go libraries cover the need
for data handling (GoLearn), binary classification problems (Hector),
and passing data (Goml). Also, Golang has Theano, a library
similar to Python’s TensorFlow. Theano provides Go developers with pieces of
algorithms that can be reused.
Good at
scale and computations
Unlike Python, Golang scales and performs well within large-scale
projects. Another reason to use Golang for AI programming is its speed,
especially when it comes to the speed of math computations. To compare, Go copes
with complex math problems up to 20-50 times faster than Python.
Minimalism
and good code readability
Most of Go’s algorithms stick to a minimalist approach. This allows
developers to create very readable code after algorithm implementation. Yet,
this minimalist approach can also be a weak point. For instance, when there’s a
need for recursive algorithms, which can run slower due to the absence of
tail-call optimization.
Golang
downsides for AI programming
Talking about weak points, we suggest looking at these two.
Need for
deep AI expertise
Some Golang advantages for web development can play against it regarding
AI programming. For instance, default multithreading is helpful for Golang web
development. However, using multithreading for AI purpose requires seasoned Go
developers with deep expertise in data science.
Golang
toolkit extension in progress
Of course, Go has its libraries for AI and is capable of covering the
most essential purposes, yet the toolkit is not as extensive. Basically, it’s
in the process of extension along with Golang community itself. So far, Golang
developers have performed about 285k pull requests globally, according to GitHub.
Final
thoughts
Python keeps striking the list of the most demanded languages for AI
programming. However, Golang is expanding its territory gradually. So far, Go
has served great for web apps. Now it also has good potential for AI
programming. Clean codebase, reused algorithms, and good scalability makes
Golang a great technology for AI. As Golang company, we truly believe its
growing community will contribute much to overall AI programming.
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