The Java ecosystem now offers a wide variety of ML frameworks - from lightweight toolkits for data mining to full-fledged deep-learning engines - making it easier than ever to build ML applications on the JVM.
Many of these frameworks support both classical machine learning (classification, clustering, regression) and modern deep learning workflows, catering to beginners and enterprise-grade projects alike.
Integration with JVM, scalability for large datasets, and compatibility with big-data tools make Java ML frameworks a compelling choice for production systems and real-world applications.
Java is still considered an important language for machine learning, not only for legacy systems and the enterprise but also for future technologies in ML, data science, and AI. The reason is strong libraries and frameworks designed for the JVM; thus, a developer who loves Java can build a classification model, a clustering pipeline, and even a deep neural network, and deploy them at scale.
Java is a fundamental language for machine learning, and here is a list of the best tools to help create classification models and more:
Deeplearning4j is a potent open-source deep learning framework built for Java and other JVM languages. It can train several types of neural networks, including convolutional networks (CNNs), recurrent networks (RNNs), and autoencoders. Therefore, it is suitable for applications that require image recognition, speech processing, and time-series analysis.
Also read: Why Learning More Than One Programming Language is Better for Coding
Weka is a comprehensive machine-learning toolkit widely adopted for data mining and classical ML applications. It comes with a wide range of algorithms for classification, regression, clustering, and association rule mining. Weka is very popular in the academic environment and for quick prototyping due to its easy-to-use interface and simple API.
Smile is an astonishingly fast and versatile machine learning toolkit for Java, featuring numerous supervised and unsupervised learning algorithms, clustering, classification, and visualization tools. The combination of its power and flexibility makes it a major option for developers seeking a state-of-the-art, effective, Java-supportive ML library that can handle production workloads without the need for deep learning frameworks.
Tribuo is a contemporary machine learning library in Java that emphasizes factors such as sustainability, reproducibility, and enterprise-grade fitness. It is feature-rich with classification, pumping up the regressions, clustering, and detection of anomalies, as well as offering stringent type safety and inherited data proving the source tracking. Furthermore, Tribuo also supports integrating models developed on platforms such as TensorFlow and ONNX.
Also read: Best Swift programming books to start learning today
Apache Spark MLlib is a well-known machine learning library for Java that provides scalable machine learning capabilities through Java APIs, making it well-suited for big-data projects. They are available in MLlib, which provides implementations of algorithms for classification, regression, clustering, and collaborative filtering. Its distributed processing power and compatibility with Spark pipelines make it a perfect choice for enterprises handling large datasets.
Java developers now find the ML ecosystem to be the strongest it has ever been. Deep learning and heavy use cases are Deeplearning4j’s territory, while Weka and Smile are excellent choices for classical ML or quick prototyping.
Tribuo will guarantee enterprise-grade, maintainable workflows, and Spark MLlib will give big-data applications for ever-increasing data the utmost power. The selection of the right tool depends on the project's objectives. Still, Java's ML frameworks ultimately provide the user with a complete toolkit for moving from simple predictive models to large-scale AI solutions.
Is Java still relevant for machine learning in 2025?
Indeed, the Java ML frameworks today are capable of classical ML, deep learning, big-data workflows, and production deployment.
Can I build deep neural networks with Java?
Definitely, deeplearning4j supports CNNs, RNNs, and autoencoders, and integrates with the big-data ecosystem for scalable training.
Which tool should a beginner start with?
Weka and Smile are the best options for novices, as their setups are simple and support commonly used ML algorithms.
Do these frameworks support distributed computing?
Yes, it is so. Deeplearning4j supports distributed training, while Spark MLlib efficiently handles large-scale distributed ML workloads.
Can Java ML frameworks be used in production systems?
Indeed, the Java ML frameworks can leverage classical ML, deep learning, big-data workflows, and production deployment simultaneously.