What Makes Python An Ideal Choice For AI App Development

When it comes to cross-platform SaaS application development, Python is amongst the most extensively used programming languages. Being one of the oldest high-level languages, it is still preferred by a large community of developers for building feature-rich software applications, engaging user interfaces, games, and 3D applications. It is also a preferred choice for network-based computer programming as well as automation testing. 

Python remained the best programming language for implementing AI and Machine Learning algorithms in various software applications. However, with the advent of a series of advanced frameworks and SDKs, JavaScript became more popular for AI app development. While both these languages are well suited for a seamless implementation of AI algorithms, Python gives you some unique benefits. 

At Oodles Technologies, we have experienced developers who are skilled at using both JavaScript and Python frameworks for AI app development. Over the years, we have observed that clients are taking more interest in Python-based development. The reasons are quite straightforward. With Python, they get more flexibility, better support, and easy upgrades. 

Also read Understanding Machine Learning Algorithms In Python

 

Reasons For Choosing Python

Python-based frameworks like Flask and Django are preferred by developers for building a variety of software applications for varied project requirements. As already mentioned above, developers can use this high-level programming language to create web and mobile applications, games, user interfaces, graphic designs, prototypes, and more. Nevertheless, Python app development is best-suited for building AI and ML-based applications. Let’s delve into the main factors that make Python an ideal choice for AI and machine learning application development services. 

An Extensive Library Support

With Python, developers gain access to a broad library of programming functions that is extremely useful for implementing AI algorithms. A library is a group of modules published by various sources to implement different types of functionalities in an application. Each module in a library is a pre-written piece of code that enables developers to implement a particular feature or functionality. The availability of these modules reduces development complexities and saves a significant amount of time, thereby accelerating the coding process. 

Python renders support for several full-fledged AI and machine learning libraries, making it effective for AI app development. Some of the most popular AI libraries in Python include:

• Keras: for implementing deep learning algorithms

• Tensorflow: for implementing deep learning, training artificial neural networks and handling massive chunks of data to extract information

• PyBrain: to implement artificial neural networks and train algorithms using unsupervised learning

• Pandas: to perform high-level data optimization and analysis

• Scikit-learn: to train machine learning algorithms using supervised and unsupervised learning

Better Flexibility and Readability

Flexibility is yet another compelling reason for using Python for AI and machine learning application development. For example, you can easily switch between scripting and object-oriented programming based on the project requirement. Besides, developers can easily implement new functionalities without having to recompile the entire source code. In fact, they can implement changes and see the results in real-time. Furthermore, Python-based codes are easy to read and understand which enables development professionals to conduct seamless code reviews.  

Multi-platform Development

Python lets you develop high-quality software applications for virtually any platform including Windows, macOS, Android, and Linux/Unix. Developers can easily transfer an application from one platform to another with minimal code changes. In addition, they can use several Python-supported packages PyInstaller to make their code executable for different platforms. Using these packages simplifies the cross-platform development processes, reduces time-to-market, and reduces development complexities.

Multi-platform support is yet another compelling reason that fosters the use of Python for AI application development. Developers can easily shift their AI-based application from one platform to another by making several small-scale changes in code. In this way, they don’t have to write the entire code from scratch which saves time and effort. 

You may also be interested in reading TensorFlow Vs H2O: The Best Enterprise-grade Machine Learning Tool

 

Vast Community Support

Developing a software application can be challenging at times and having seasoned professionals at your disposal is definitely an advantage. Python gives you an upper hand over your development project by providing vast community support. Since it is an open-source language, there are ample resources and programmers available to help you deal with complex several problems. Besides, Python documentation is readily available online and across the development communities. There are several online forums where developers can collaborate to discuss their problems and help each other solve them quickly. 

The Final Word

The aforementioned factors clearly indicate the effectiveness of Python-based programming for AI and machine learning application development. Due to these unique offerings, Python continues to grow in popularity and remains the first choice of developers for implementing complex AI algorithms. 

Why Choose Oodles Technologies For Python-based AI App Development?

We are an experienced SaaS development company that builds high-quality software applications using Python to support multiple platforms. We formulate effective development strategies based on the project requirements and use agile methodologies to build user-friendly software applications. Our development team is skilled at using the latest Python frameworks including Flask and Django to create feature-rich applications that maximize user engagement and interactivity.