Beyond Python: Exploring Modern Languages for Machine Learning and AI
Top Programming Languages for AI/ML in 2023
With the rapid growth of machine learning and artificial intelligence, Python has become the de facto language for data scientists, machine learning engineers, and AI researchers. Its vast ecosystem of libraries, frameworks, and tools, combined with its ease of use and readability, have made it the go-to choice for many in the field.
However, as the complexity and scale of machine learning and AI projects continue to increase, there is a growing need for languages that can handle the demands of these applications. Beyond Python, there are several other languages that are gaining popularity in the machine learning and AI community.
In this blog, we will explore some of these modern languages and the unique advantages they offer for machine learning and AI applications. We will take a closer look at languages such as Julia, Mojo🔥, Rust, and Go, and examine how they compare to Python in terms of performance, scalability, and ease of use.
Whether you're a seasoned data scientist looking to expand your toolkit or a beginner exploring the world of machine learning and AI, this blog will provide valuable insights into some of the most promising modern languages for these fields.
High-performance Languages for Machine Learning
Python
Python is a popular programming language for data science due to its many advantages. Its simple and intuitive syntax makes it easy to learn and read, and its large and active community provides support, documentation, and resources. Python also offers powerful libraries for data analysis and visualization, such as pandas, numpy, matplotlib, and seaborn, as well as excellent machine learning libraries like scikit-learn, TensorFlow, and PyTorch, enabling the building of complex models and algorithms.
However, Python also has some drawbacks for data science, such as being slower compared to other languages like C or C++, having memory limitations, and version compatibility issues with different versions of Python not being compatible with each other or with some libraries. Thus, while Python is good for data science because it is easy to use, versatile, and rich in features, it is also bad for data science because it is slow, memory-intensive, and prone to compatibility issues.
In addition to its drawbacks, Python also has several ways to overcome its limitations in data science. For instance, it can utilize JAX, a library that can compile and run Python and NumPy code on accelerators like GPUs and TPUs using XLA. JAX also provides automatic differentiation and vectorization features, making it a useful tool for machine learning and scientific computing.
Python can also leverage Numba, a library that can compile Python code to native machine code using LLVM. Numba can speed up numerical computations and support parallel processing, further enhancing Python's capabilities in data science.
Moreover, Python can use Dask to leverage distributed computing or implement multi-threading and multi-processing concurrent Python programs to speed up the runtime. These tools and techniques can help mitigate Python's limitations in terms of speed and memory usage, making it a more efficient language for data science tasks.
C++
C++ is a programming language that is well-suited for data science, offering several advantages. Firstly, its speed and ability to quickly compile data make it an essential tool for managing large and intricate datasets. Secondly, its low-level nature allows developers to delve deeper into applications, enabling them to fine-tune specific aspects that may be otherwise impossible. Additionally, C++ can be used to create new libraries for data science that can be integrated with other programming languages, such as TensorFlow, PyTorch, and Scikit-learn.
Despite its benefits, C++ also has some drawbacks that must be considered in the context of data science. One of the most significant challenges is its steep learning curve, which is due to its complex and verbose syntax that requires an abundance of boilerplate code. Furthermore, C++ has a relatively smaller and less active community compared to other popular programming languages like Python and R, resulting in fewer resources, documentation, and support. Lastly, C++ lacks built-in features for data analysis and visualization, such as statistical functions, data structures, and plotting tools.
In summary, C++ is a valuable language for data science because of its speed, low-level capabilities, and capacity for developing new libraries. However, it also has some limitations, such as its complexity, smaller community, and lack of built-in features. Therefore, it is essential to weigh the pros and cons of C++ when considering it as a tool for data science.
Julia
Julia, which is a high-performance language designed for numerical and scientific computing, offers many advantages over Python, including faster performance, easier parallelism, and better built-in support for numerical computations. It was developed and incubated at MIT and it is free and open source. It offers a number of features that are well-suited for machine learning, such as high performance, easy parallelism, and built-in support for linear algebra. Its math-friendly syntax makes it easy to write and read code, and it can interoperate with other languages such as Python, C, R, and LaTeX. However, as a relatively new language, Julia may have some bugs or compatibility issues in future versions. It also has a smaller community and fewer resources than languages like Python and R, and suffers from long compilation times, which can slow down development. Julia has many packages for machine learning, such as Flux.jl, Knet.jl, MLJ.jl, etc
Check out the introduction video from Fireship:
Mojo
Chris Lattner is the creator of Swift and LLVM who founded a company called Modular to build Mojo, a new programming language for AI applications. Mojo aims to combine the usability of Python with the high performance of C and facilitate easy integration with various AI frameworks and hardware like CPU, GPU, TPU etc. It is a superset of Python, meaning that it is fully compatible with the Python ecosystem while providing low-level performance and control, compile-time metaprogramming, and simple integration with different AI frameworks and hardware.
Mojo is built on top of MLIR, a compiler infrastructure that enables adaptive compilation techniques and supports heterogeneous systems. It is designed to target accelerators and other systems that are pervasive in machine learning, making it the future of AI programming.
I just learn this programming today on youtube for their Product Launch 2023 Keynote
System Programming Language
TLDR; System Programming Language might not be the perfect candidate for doing the machine learning modelling part, but they could do the data processing fast and efficiently. However, they are not the only ones that can do so, and there may be better alternatives depending on the situation.
Rust
Rust is a programming language that can be a good fit for data science in certain situations. Its speed and memory-efficiency make it well-suited for handling large and complex datasets without wasting resources or crashing. Rust's reliability and security features ensure memory-safety and thread-safety, avoiding common errors and vulnerabilities that can plague other languages. Additionally, Rust's interoperability and extensibility allow it to easily integrate with other languages and frameworks, such as TensorFlow, PyTorch, NumPy, and SciPy.
However, Rust may not be the best fit for all data science tasks due to its steep learning curve, lack of maturity and stability, and mismatch with the data science workflow. Rust's complex syntax and semantics may be intimidating for beginners, and its smaller community and ecosystem may result in fewer resources and support for data science. Additionally, Rust's low-level focus on performance and control may not align with the high-level exploration and experimentation typically required in data science tasks.
Go
GO is a programming language that has both advantages and disadvantages for data science. Go is a statically typed, compiled high-level programming language designed at Google. GO is good for data science because it is fast and concurrent, simple and readable, and scalable and portable. With performance comparable to C and C++, GO can handle large and complex datasets without wasting resources or blocking other processes. Its minimal and consistent syntax is easy to learn and write, and its powerful standard library provides useful packages for data science. GO also has a modular design that allows it to scale up or down based on project needs, and a growing ecosystem of libraries and frameworks for data science.
However, GO is bad for data science because it has a lack of maturity and diversity, a mismatch with the data science workflow, and a trade-off between simplicity and expressiveness. As a relatively new language, GO's community and ecosystem may have fewer resources, support, documentation, tools, libraries, and frameworks for data science than other languages. Its low-level focus on performance and control may not align with the high-level exploration and experimentation typically required in data science tasks. Additionally, while GO's simple syntax avoids unnecessary complexity and verbosity, it may lack some features and constructs that other languages offer for data science.
Overall, GO can be a good fit for data science when speed, concurrency, simplicity, scalability, and portability are important factors, but may not be the best fit when maturity, diversity, compatibility, and productivity are the primary concerns.
The list of modern languages for data science is constantly evolving, and many promising languages could be worth exploring. Stay tuned for part two of the list. If you have any suggestions or recommendations for languages that you think are worth mentioning, please feel free to share them in the comments section below or send me a message on LinkedIn. 📩
