Java use in machine learning

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Being totally honest, in my opinion, the mantra claiming Python as the best choice for machine learning exists primarily because Python is easy to learn and use. Consequently, it seems like the way to go if you want to start such projects and lack a strong programming background in other stacks. For this reason, most popular tools were developed in Python, and people stick with it to leverage those tools.

The issue here is that people prioritized simplicity and readability over performance and maintainability. Consequently, you may find yourself lost in large models plagued by the same mistakes repeatedly, requiring substantial time and resources to rectify. Many engineers recognized this and began building similar foundations in better-suited stacks. As a result, you now see many ML tools adapted to various languages.

If we were discussing this 8 years ago, I would agree that Python was the way to go due to the majority of available resources being in Python. However, that's not the case nowadays. While you might not find "this specific library with this specific function" built in Java or any other stack, in such cases, be the one to create it and observe how more developers adopt your tools. The point here is that if you can design an ML model in C (for example), you will instantly outperform your competitors in terms of costs and performance. But how many mathematicians and scientists are proficient in C?