Rust is a programming language that has seen a significant increase in popularity in recent years. According to the State of the Developer Nation report by SlashData, the number of Rust programmers has grown from 600,000 in the first quarter of 2020 to 2.2 million in the same period of 2022.
One of the reasons Rust is a great choice for machine learning application development is its emphasis on safety guarantees. This means that developers do not have to worry about memory corruption bugs or other errors, making it a more efficient choice compared to C++ or C# when building complex apps.
Additionally, Rust offers unique features such as pattern matching, ownership, and type inference which make the code easier to read and understand, thus increasing its readability and maintainability. Let’s review some of the main advantages of using Rust for ML development.
One of the main advantages of Rust is its memory safety. Rust is a systems programming language, which means it was designed to be used in low-level systems programming such as operating systems, device drivers, and embedded systems. As such, it has built-in memory safety features that help prevent common programming errors such as buffer overflows and data races. These features make it much harder to introduce bugs that could cause crashes or security vulnerabilities. This is especially important in ML app development, as bugs in the underlying infrastructure can have serious consequences.
Another advantage of Rust is its performance. Rust is known for its fast execution time and low memory usage, which makes it ideal for ML apps that need to process large amounts of data quickly. Rust’s performance is comparable to C and C++, but it provides a more modern and expressive syntax, which can make it easier to write and maintain large code bases.
Rust also has a large and growing ecosystem of libraries and frameworks that make it easy to get started with ML app development. For example, the ndarray library provides an n-dimensional array type that is well-suited for numerical computing, and the TensorFlow library provides a Rust API for the popular TensorFlow library. Additionally, the Rust community is actively working on improving support for ML in the language and ecosystem, which means that new libraries and frameworks are being developed all the time.
Finally, Rust’s strong type system and compile-time safety checks make it a great choice for ML app development. Rust’s type system is designed to catch many common programming errors at compile-time, which can save a lot of time and effort in the debugging process. This is especially important in ML app development, as ML models are often complex and hard to debug.
Rust is a great choice for ML app development for many reasons. Its memory safety features, performance, growing ecosystem, and strong type system make it an ideal choice for building robust and efficient ML apps. As the demand for ML continues to grow, Rust is well-positioned to be a valuable tool for ML engineers and developers.