Python for AI/ML

Python has established itself as the go-to programming language in various fields, but most notably in AI, ML, and building applications powered by large language models (LLMs). Python offers simplicity, readability, and a robust ecosystem of libraries, making it an ideal choice for all kinds of users, whether they are developers, researchers, or even students just getting started with programming. Python has also emerged as the language of choice for building new LLM-powered applications, underscoring Python’s usefulness, popularity, and versatility.

In this section, you will learn some of the reasons that make Python a great choice for building modern AI-powered applications:

• Simplicity and readability: Python’s syntax is designed to be intuitive and clear, which is one of its core strengths. Python can represent complex algorithms and tasks in a few lines of code that are easily readable and understandable.
• Rich ecosystem of libraries and frameworks: Python offers an extensive range of libraries and frameworks specifically designed for AI/ML use cases. Libraries such as TensorFlow, PyTorch, and scikit-learn have traditionally been popular for ML tasks. Hugging Face’s Transformers library has also become an indispensable part of the developer workflow for building modern LLM-powered applications. It provides pre-trained models and straightforward APIs to fine-tune models for specific tasks. These libraries not only accelerate development time but also provide cutting-edge solutions to developers across the world.
• Strong community and support: Python is one of the most popular programming languages in the world, and hence has a huge community. According to the Stack Overflow survey 2023 (https://survey.stackoverflow.co/2023/), it’s the second most popular programming language after JavaScript (excluding HTML/CSS). This strong and large community provides a wealth of resources, including tutorials, discussion forum engagements, and open source projects, which offer a helpful support system for someone working on building modern applications.
• Integration with other technologies: Python’s ability to integrate seamlessly with other technologies and programming languages makes it a great choice for AI/ML tasks and building LLM-powered applications. For example, Python can easily interface with programming languages such as C/C++ for performance-critical tasks. It also interfaces well with languages such as Java and C#. This flexibility of Python is helpful for deploying LLM-powered applications in diverse environments, ensuring that Python can be part of large heterogeneous systems.
• Rapid prototyping and experimentation: Building a sophisticated AI/ML-powered application requires many iterations of tests, experiments, and fine-tuning. Python allows developers to quickly build prototypes in a few lines of code. Easy testing and debugging also help to prototype a quick solution. Python’s interactive environments, such as Jupyter Notebook, provide an excellent platform for this purpose. With Python, developers building LLM-powered applications can quickly test hypotheses, visualize data, and debug code in an interactive manner.

Python combines speed, simplicity, specialized libraries and frameworks, and strong community support with easy integration with other languages and technologies, all of which make it an excellent choice for building modern LLM-powered applications.