AI programming has become a lot more than software and hardware for development firms. Companies in e-commerce, real estate, and healthcare are some of the industries that are beginning to implement AI.
Python and Golang are two of the favorite programming languages for AI and it isn’t easy to choose the best option. This article will clear up some of the confusion when it comes to choosing between them.
What Python brings to the AI programming table
While no programming language is great, Python ticks a lot of boxes, the following list goes through some of them:
A large set of libraries
With the Python libraries, you can-
- Build new algorithms- LightGMB
- Perform model prediction- Eli5
- Process datasets- Keras
- Work with complex data -Scikit-Learn
These are just a few of the possibilities. One other to mention is Tensorflow, an open-source library used by a lot of people for its Google’s machine learning apps.
The community bond
GitHub annual statistics stated that in the previous year, there were more than 1 million pull requests within the vibrant and active Python community. This community doesn’t just share ideas, it also helps to create new libraries, update documents and extend toolsets-
Language accessibility
When we talk about the accessibility of Python we understand that this is constantly growing. Businesses have access to a huge number of Python experts. As a programming language, it was ranked the best by the Institute of Electronic and Electrical Engineers.
The negatives of Python
As we mentioned before, no program is perfect and as a programming language for AI, even Python has a few.
Not great for large-scale engineering
If it comes down to a few hundred programmers, Golang beats Python in scalability. If you are under pressure for an ordered, strict method of programming, Python will be difficult. The same is said if you want to deploy highly sophisticated AI systems.
Codebase could be difficult to maintain
Python doesn’t always play nicely with all of the libraries, support systems, and third-party integrations.
We are missing some multicore processing and performance
Python is challenged when it comes to its performance, in particular with CPU and GPU processing. It isn’t the be-all and end-all as there are ways around it, manly tweaks.
There are too many versions
This is a sore subject for most developers. You will run into problems when you move to different versions, disconnect between Python 2 and Python 3 or when you have various versions running at the same time.
Finally, developers are often challenged by packaging systems. Each version has a packaging system that works in a different way. You might need to install different packaging systems in multiple environments.
What Golang Brings to the AI development table
Is it enough for Golang to take the head seat? We will now look at the pros and cons of Golang.
Libraries written in Go are good for Go developers
A developer using Go doesn’t have to use libraries in other languages. Fewer pieces from different languages add to the comfort of the developer.
It covers a large number of purposes
Golang many not be able to compete with Python’s number of libraries but they do cover an excellent range of purposes:
Data Handling –GoLearn
Binary classification problems- Hector
Passing data- Goml
Where Python has TensorFlow, Golang has Theano, offering pieces of an algorithm to developers for reuse.
A decent level of scaling and computations
Python isn’t suitable for large-scale projects but Golang is. With AI programming, Golang can handle bigger projects and with speed. Go can handle difficult maths problems 20-50 times faster than Python.
A minimalistic approach and good code readability
The less is better allows developers to create very readable codes after the algorithm has been implemented. So might see the downsides to this too, for example, slower running because of a lack of tail-call optimization.
The negatives of Golang
Although there are only two main negatives, they are worth going over.
User ability
Sometimes Golang is its own worst enemy. Take for example default multithreading. It is an excellent tool for Golang web development but you need to have expert skills in data science to use multithreading for AI purposes.
The Golang toolkit is in progress
Golang’s toolkit is still in development. You will find numerous options to suit your needs in the libraries but it has a little way to go. The same can be said with its community. GitHub recorded developers performing 285k pulls globally.
In Conclusion
Python is obviously up there as one of the best languages for AI programming but Golang is definitely starting to grow in popularity. It gained fame in web app development and has started to do the same in the AI field. It already has a clean codebase, reusable algorithms, and good scalability. As time passes and its community grows, Golang will have a great impact on AI programming.
