"C++ is used to create many of the libraries Python draws on, so its presence in AI/ML is established. But what about Java, Rust, Go, and C#/.NET? All have a major presence in the enterprise programming world; shouldn't they also have a role in AI and machine learning? Java In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe. Spark pushed the limits of what Java could do, and newer projects continue to expand on that. One example is the Apache Flink stream-processing system, which includes AI model management features."
"The Java universe-meaning the language, the JVM, and its ecosystem (including other JVM languages like Kotlin)-provides a solid foundation for writing machine learning and AI libraries. Java's strong typing and the speed of the JVM mean native Java applications don't need to call out to libraries in other languages to achieve good performance. Java-native machine learning and AI libraries exist, and they're used at every level of the AI/ML stack."
Python is the default choice for AI and machine learning, while C++ underpins many core libraries. Java previously played a central role in ML and AI, with platforms like Apache Spark originating in the Java ecosystem and newer projects such as Apache Flink adding AI model management. The JVM, Java's strong typing, and runtime speed enable native, high-performance AI/ML applications without cross-language calls. Java-native libraries and integrations exist across the stack, including Spring AI, Spark MLib, and GPU-accelerated libraries like GPULlama3. Other enterprise languages such as Rust, Go, and C#/.NET are mentioned as potential contributors to AI/ML.
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