AI or Artificial intelligence is everywhere. From writing our emails to recommending things to read, products to buy, and foods to eat, AI is helping us become efficient and live a better life every day. It is not a secret that the astonishing pace of recent advancement in AI has been significantly aided by the existence of Python. It is also not a stretch to say Python, intended or not, was built for AI. Here are four(4) reasons why:
1. Powerful Libraries
Developing robust AI solutions requires a vault of stable and battle tested libraries that can be quickly deployed to do all the heavy lifting. Python’s arsenal of neural network, image processing, data processing and numerical libraries were built just for that. With Tensorflow, Keras, PyTorch, Therano, Sci-Kit, just to name a few, a developer can effortlessly handle sophisticated computational tasks and construct neural network for mathematical expression optimization, image recognition, natural language processing, pattern recognition and a lot more. NumPy and Pandas makes it easy to perform matrix algebra and manipulate large dataset. Mat-plotlib adds data visualization capabilities that developers need to describe data and models creatively and intelligibly. Most importantly, all the libraries can be added seamlessly as needed without incurring overheads.
2. Rapid Deployment
According to research a developer can be twice as productive working with higher level languages such as Python than C or C++ because of higher number of instruction per line coded. Meaning developers need to write less code in Python than in C++ to do the same task.
From the people who developed the language puts it technically as follows –
“[Python’s] high-level built-in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components.”
In business language it means, Python reduces the time to market when you are developing an application. This is also precisely why Python is favored among the AI/ML engineers and data scientists. When you are working with large and complex dataset you need to see the result quickly and often to test assumptions and see convergence.
Commercial and enterprise AI/ML solution run on cloud GPU clusters mostly operated by Linux or other Unix derivatives. Developing an AI solution on such clusters is often inefficient and always expensive. It is much easier to develop the solution in a PC with a reasonable GPU and deploy in production like any other web application. This however requires the code must be portable between development and the production environment. Python by design offers such portability. Whether the development environment is Windows, MacOS or Linux, python code can be deployed in production in any operating systems as if the code was written for that OS. With Python AI engineers and data scientists can simply focus on developing and perfecting their models and not have any worry about deployment.
No discussion in Python is complete without discussing the community behind it. In survey after survey, Python is one of the most popular programming languages not only because it is one of the easiest to learn but also because of its large number of open source libraries. There are approximately 140,000 python libraries to build anything imaginable in any fields. Additionally, there are number of Active Python developer communities an Python Software Foundation is active in documentation, promoting adoption, organizing regular conferences, and continuous development of the language.
Finally, the core of AI/ML and data science is a three step process – 1) data preparation 2) feature engineering and 3) training & validation. In all of these steps Python provides the libraries and support that are often unmatched by any other languages.