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Why You Should Consider Swift For Machine Learning

Why You Should Consider Swift For Machine Learning

Sameer Balaganur
Swift for machine learning

Recently, Swift 6, the team behind Apple’s popular programming language Swift, announced that it is planning to open up the programming language for machine learning. Swift has already established itself when it comes to executing at the production level with faster speed than most of the languages. 

When one hears Swift, it probably takes them to app development for iOS, and if one is familiar with machine learning, they might have heard about Swift for Tensorflow (S4TF). But, one might wonder, despite the widely used languages like Python or C++ or Java for that matter, why use Swift for Machine Learning?

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Swift Over Python

For some time now, Python has been the number one choice for machine learning because of its ease of use and low barrier of entry. However, today, programing languages such as Julia, Rust, among others are getting into the market due to the requirement of high-performance computing in data science. And now Swift is rapidly moving towards machine learning to assist developers in writing production-level code that has data science capabilities.

Since Python is slow, it has opened up the opportunity for other programming languages to tap in the data science landscape. Swift, in general, is around 8.4x times faster than Python. And because the types aren’t checked in Python, the program might encounter a type error at run time and may crash. In contrast, the possibility of that happening with Swift is less because it is a statistically typed language.

Let’s take a look at what features Swift offers

The Speed

When one talks about the fastest among the programming languages, usually one name comes up, and that is C. It is highly optimised and achieves excellent speed. But, it is not easy to learn and also, the speed is a trade-off with C not being memory safe.

Now, one should know, Swift for numerical programming wasn’t explored much, so when it was established that Swift works fast on mathematical computations. Swift is as fast as C and without the memory safety issues, so naturally, it became more popular and is now touted as the next big language for machine learning. Though it is relatively new, Swift has been increasingly integrated into many systems.

The reason behind Swift being powerful is the LLVM compiler, and it has efficient optimisations that will ensure the code runs fast. 


Swift is at an early stage in machine learning; as a result, not a lot of machine learning libraries are available for it, but its interoperability with Python, C and C++ compensates for the lack of libraries. If one wants to implement a specific functionality into Swift, one can simply import it from corresponding C, C++ or Python. One can import Python libraries in Swift, and that does the work.

Swift’s Differentiable Code

For S4TF (Swift for TensorFlow), differentiable code is integral to the product. So, when one trains their machine learning model, and it involves calculus, which has an unreliable rule called the Chain Rule. So what differentiable programming is doing is that it is automatically computing the chain rule in the language, which makes it highly scalable and open to experimenting on new things or build on the existing libraries. It makes the training an open box in terms of giving one the freedom of tinkering with the process smoothly.

Inspecting the Code

Inside Python for TensorFlow library, it consists of C code. So, when one wants to see how some functions are implemented in Python, they won’t be able to see it, because the Python code for that function is implemented in C. In contrast, with Swift, because there is no C implementation, one can see how something is actually implemented. 


As Swift grows in popularity, it does have some problems to counter, like instability.

A lot of developers have this common problem with Swift that it is unstable. It means that whenever there is a new version of this language, one will have to rewrite the entire project in the latest version. This becomes a hassle with both app development and machine learning. 

And not only that, but Swift battles the lack of talent. Because it is relatively new, people still need to learn and move on from other popular languages like Python.

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