Python vs Golang : Which is Better for AI/ML Projects?

In this article, we'll explore Python vs Golang : Which is Better for AI/ML Projects?

The two distinct fields stand out as leaders in influencing the future of  technology and innovation in the quickly changing landscape: robots and data science. For those hoping to have a significant effect on their particular sectors, these fields have great promise and provide exciting career options.

However, when considering which career path to pursue, it's essential to understand the unique characteristics, opportunities, and challenges associated with each field. In this article, we'll explore Robotics and Data Science to understand which career is promising in 2024.

1. Python:

Overview:

Python is a high-level, definable programming language known for its simplicity and readability. Developed by Guido van Rossum and first released in 1991, Python gained a reputation for its versatility and ease of use.

Pros:

Strong ecosystem: Python has a huge ecosystem of libraries and programs specifically designed for AI and ML, such as TensorFlow, PyTorch, scikit-learn, Keras and these libraries provide a great deal of functionality approximate, train and implement machine learning models.

Ease of use: Python is known for its simplicity and readability, which makes it easy for beginners to understand and write code.

Community support: Python has a very active community supporting various AI/ML projects. This means you can easily find resources, tutorials and support online.

Cons:

Performance: While Python is great for prototyping and testing though, it may not be as efficient as a language like Go, especially for tasks that require a lot of computers. The Python Global Interpreter Lock (GIL) can also restrict the ability to fully utilize multicore processors.

Concurrency: Python threading model may not be suitable for high concurrency scenarios due to GIL. Although libraries like asyncio are available for asynchronous programming, they may not perform as well as Go's concurrent counterpart.

Deployment Overhead: Installing Python applications may require managing dependencies and resolving version compatibility issues, which can add overhead, especially in production environments.

2. Go (Golang):

Overview:

Go, frequently known as Golang, is a statically typed, compiled programming language designed for simplicity, efficiency, and concurrency. Developed via Google engineers in 2007 and primary launched in 2009, Go has won recognition for its ease of use, strong performance, and built-in support for concurrency.

Pros:

Performance: Go is known for its speed and efficiency, resulting in AI/ML tasks that require intensive computation

Concurrency: Go's light-weight concurrency primitives (goroutines and channels) make it easy to put in writing concurrent packages, allowing green parallel processing of records.

Static typing: Go is statically typed, which can help catch compile-time errors and improve code robustness, especially in large projects.

Deployment: Go programs can be compiled as independent binaries, eliminating the need to maintain dependencies in the deployment environment. This simplifies deployment and distribution.

Cons:

Limited ecosystem: While Go is gaining popularity in the AI/ML community, its ecosystem is not as mature as Python. There are few libraries and frameworks for machine learning tasks, although this will improve over time.

Learning Curve: Go's syntax and ideas can be unusual to developers coming from dynamically typed languages like Python, that may bring about a steeper mastering curve.

Community Size: While Go has a developing community, it is not as large or as active as Python's, which could affect the supply of sources and community aid.