When you think of AI and machine learning, Python probably comes to mind first. It’s become the go-to language because it’s simple, versatile, and has a huge community. But other popular languages like Java, Rust, Go, and C#/.NET are also making waves in the AI world. They’re used in different ways, depending on what’s needed. Let’s take a look at how these languages fit into AI and machine learning today.

Java’s Role in AI and Machine Learning

Java was once the main language for AI and data science before Python took over. Big projects like Apache Spark, which helps process large datasets, started in Java. Spark’s capabilities pushed Java’s limits, and newer tools keep building on that foundation. For instance, Apache Flink is a streaming system that includes features for managing AI models, all built in Java.

Java’s strong typing and the speed of the JVM (Java Virtual Machine) mean that native Java apps can perform well without needing to call external libraries. There are several Java-native libraries for machine learning, like Spring AI, which helps developers integrate AI into their apps. Apache Spark’s MLlib allows scalable machine learning tasks, and libraries like GPULlama3 enable GPU-accelerated AI computations in Java.

However, Java isn’t perfect for experimenting with AI models because its compile and run cycle can be slow. This makes it less ideal for testing new ideas quickly but excellent for building stable, scalable AI infrastructure.

Rust’s Growing Impact in AI

Rust is a newer language, just 13 years old, but it’s already gaining popularity. Known for speed and safety, Rust is great for developing reliable data science tools. Many tools in the data science field now include Rust components. For example, Polars is a fast dataframe library similar to pandas, and it’s used in multiple languages.

Rust also has dedicated AI and machine learning projects. Tract, for instance, allows running inference on models in the ONNX format. Burn is a deep learning framework that leverages Rust’s performance and safety features to build models optimized for different back ends. These tools are designed to be used within the Rust ecosystem, rather than just exporting data to other languages.

The main challenge with Rust is its compile times. Building large projects can take a while, especially with many dependencies. This makes Rust less suitable for quick experiments but very useful for creating robust AI libraries and back-end services.

Go’s Niche in AI and Machine Learning

Go, developed by Google, is known for its speed and simplicity. It compiles fast and runs smoothly, making it great for experimenting and testing new ideas. That said, Go’s ecosystem for AI isn’t as mature as Python’s or even Java’s.

As of recent years, there have been some native AI libraries for Go, but they’re limited. Golearn is one such library, but it hasn’t been updated in years. Gorgonia aims to be similar to TensorFlow but also lacks recent updates. Many AI projects in Go focus on serving models or working with third-party APIs rather than developing new models from scratch.

Most of Go’s strength in AI lies in deploying models or building infrastructure rather than creating AI models. It’s a good choice if you want to integrate AI into services or applications rather than do experimental research.

C# and .NET’s Expanding AI Capabilities

Microsoft’s C# language and the .NET platform have evolved to include features for AI and machine learning. The latest versions, like .NET 9, come with libraries and tools designed for AI development. One standout is the Semantic Kernel SDK, which helps build applications that interact with Microsoft’s Azure OpenAI services using natural language.

These tools allow developers to work directly with data types and abstractions that match AI tasks, like large language models. This integration makes it easier to develop and deploy AI applications within the .NET environment. However, adoption outside the Microsoft ecosystem remains limited. Many developers who aren’t already using C# or .NET tend to stick with other languages for AI work.

Despite that, C# and .NET are increasingly used in enterprise settings, especially where existing infrastructure is built on Microsoft technology. They offer a solid foundation for integrating AI into larger applications and workflows.

In conclusion, while Python remains the dominant language in AI and machine learning, other languages have carved out important roles. Java continues to power big data and scalable AI solutions, Rust is perfect for building fast, reliable libraries, Go excels in deployment and services, and C#/.NET is expanding its AI toolkit for enterprise use. Each has its strengths and limitations, making them valuable tools depending on the project’s needs. The landscape is diverse, and the choice of language depends on what you want to achieve in AI development.

Inspired by

• https://www.infoworld.com/article/4078766/ai-and-machine-learning-outside-of-python.html

Sources

• gmpg.org
• apache.org
• github.com
• docs.rs
• github.com