A Python AI App must have a combination of Python frameworks, and there is no single way to create an AI app in a jiffy!
Since 1991, when it first came into being by Guido van Rossum, Python has been known as the language behind the machines. It makes machines intelligent and workable. It makes up for all apps on mobile and web, a scientific computing language for data analysis. Certain libraries of Python like Pandas and NumPy are the building blocks of data science.
Libraries/Frameworks/Tools
To make an AI based app with Python, you need to select from:
- AI/ML libraries that are a popular choice for deep learning and neural networks like TensorFlow;
- a flexible deep learning framework like PyTorch;
- some machine learning algorithm like Scikit – learn;
- something for natural language processing tasks like NLTK;
- something for computer vision tasks like OpenCV;
- something for building and deploying neural networks like Keras;
- something for executing ML tasks like classification, regression and clustering like Scikit learn; something for model building like Theano;
- something for analyzing and manipulating data like Pandas;
- and lastly something to create graphs like Matplotlib;
- Also Beautiful Soup and Requests libraries are used for data scraping.
While developing an AI app with Python, there is often a need to choose a framework for building user interface with lightweight and easy to learn frameworks like Flask, or Django.
What can be used for doing the same thing?
Like Python many other languages are used for AI app development:
- C++ is used for machine learning and neural networks, especially when latency is important. C++ is fast and can handle complex calculations, useful for AI in gaming and simulation.
- Java is used for AI development mainly for developing enterprise apps.
- Julia is used for automating memory and app that require linear algebra.
- Rust, Haskell, Ruby, Swift, Dart, Kotlin and Mojo steps in for good performance.
Often there is a need to make use of AI coding assistant tools like Sourcegraph Cody, Stable Code 3B, Amazon CodeWhisperer, and Tabnine along with Python AI Libraries like TensorFlow, NumPy, Keras, SciPy, Seaborn, Scikit-learn, Plotly, and Matplotlib, and likewise that are currently being used by AI development companies worldwide. All these libraries pre-built functionalities allow developers to quickly prototype and test AI models without starting from scratch.
How is Python helping out AI development?
Python is the language behind modern data science, data analysis, big data, machine learning algorithms due to its simplicity and readability, its concise syntax, its resemblance to English, less number of lines of code reveal a lot.
Python doesn’t require compilation that makes it suitable for faster development cycles and easier debugging.
Data scientists rely on Python for performance critical applications that speed up deployment and performance. Somtimes some performance critical parts of Python code are written in C to boost up the speed.
Ideally, Python is being used to develop apps for Windows, macOS, Linux, Web without any changes or special considerations. Infusing it with AI is possible due to vast documentation, large community, open source, free, OOPS, modularity, code reusability, and the presence of extensive data visualization libraries like Matplotlib and Seaborn that allow clear and insightful data visualization that is crucial libraries of Python for AI development.
With right tech stack of frameworks, visualization, strong community support, hassle free testing and debugging, this combination makes it a preferred language for AI development.
How to proceed with developing Python based AI applications?
Any development process should start with understanding the problem statement and what problems will the chosen technologies address. Collect relevant libraries, frameworks, databases, programming languages and develop a structure according to the prescribed architecture. Infuse features and functionalities (train the model), test, re-test, bug fix, and deploy. Implement feedback.
Combination of Python and AI is being used to build chatbots, virtual assistants as the most popular examples. It is being used to manage the computations required to create machine learning models, create charts, plots and graphs.
Identifying the problem, pre-processing the data to prepare it for training, choosing the AI model (convolutional neural network, recurrent neural network and transformers), train the model with that pre-processed data, monitor its performance on a validation set, use the evaluation results to fine tune the model.
A lot many sub-steps take place periodically during the entire process like: Time series analysis, data visualization, sales predictions, language processing, sentiment analysis, recommendation systems (like music, videos, and so on), classification, and computer vision.
Conclusive: What does future look like for Python Development Companies?
For all the reasons discussed above, Python has a simple and easily understandable syntax. It powers backend of the applications and can be relied on terms of safety. It assists many data science, and machine learning projects, complex gaming applications and is capable of delivering scalable and efficient solutions for both mobile and web applications.
This discussion cannot be closed without mentioning its rich libraries that are used for manipulating and preprocessing advanced model training. Now Python libraries are being used by python development companies to enable IoT networks, game development, data visualization, natural language processing, in the cloud;
To conclude, Python’s syntax is user-friendly and easy for data scientists and analysts to learn. Python is capable of handling large amounts of data requests. Its libraries contain pre-defined functions, classes, and methods that help programmers develop, train, and deploy AI models. The code can be used as a standalone program on many common operating systems, including macOS, Windows, and Linux. This can save time and money by eliminating the need for a Python interpreter.
