The use of artificial intelligence (AI) is growing at an exponential rate. Businesses are using AI to leverage benefits such as lower costs, increased productivity, and reduced manual errors. Those benefits are so palpable that today, 30% of all companies worldwide are using AI for at least one of their sales processes.
Python is one of the fastest-growing languages. It’s a general-purpose, open-source language that companies use for a variety of applications. Python is an interpreted and high-level language mostly used for working on the backend.
It can also be used as an extension for other scripting languages such as Pearl and Ruby. It allows programmers to code in different styles, both simple and complex.
Naturally, those definitions don’t help that much in deciding which one is the best for AI-based projects. To make that decision, you need to choose a language depending on factors such as your company’s coding standards, the libraries you’re already using, etc.
Python also has many built-in libraries for data analysis and computations. But the difference is you can also do data cleaning, processing, and analytics functions through its libraries. TensorFlow, Scipy, and Numpy are some libraries that you can use to that effect. Python also has ML frameworks like Web2py and pylon which handle data robustly.
Python doesn’t support asynchronous development and is not scalable in a conventional sense. But it does have coroutines that support asynchronous functionality. Hence it can achieve scalability too.
if your application is already primed towards Python, i.e., Python has already solved your business problem, it would make sense to go towards a complete Python solution.
Python is user friendly and relatively easy for coding. It’s a procedural programming language, i.e. it relies on a certain predefined set of steps. The commands, functions and statements are defined in a sequence. Since the steps are well structured, it’s easy to maintain and review the code.
Programmers mainly use Python for the back end and server-side scripting. Python is also used for analytics and math-intensive projects, as it’s easy to code and can be used to create wide-scale large-sized projects.
To choose one of them, you have to ask yourself, what is your existing infrastructure, which frameworks you are already using, what are your exact business requirements, what’s your budget, and what kind of skilled professionals you have in your company.