Java for Machine Learning: Capabilities and Limitations in 2024

Java for Machine Learning: Reality or Compromise?

When it comes to machine learning (ML), the first thing that comes to mind is Python with its ecosystem of scikit-learn, TensorFlow, and PyTorch. However, in the corporate world where Java dominates, the question of using this language for ML tasks is being raised more and more often. Can you build models in Java? Yes. Is it necessary to do so? It depends on the context.

In this article, we will examine what capabilities Java provides for machine learning, what limitations developers face, and in which scenarios Java might be a better choice than Python.

Main Java Libraries for Machine Learning

Java cannot boast the same number of ML frameworks as Python, but several mature and powerful libraries do exist.

Deeplearning4j (DL4J)

This is the most famous library for deep learning in Java. It supports neural networks of various architectures: from simple fully connected to convolutional (CNN) and recurrent (RNN). DL4J integrates with Apache Spark and Hadoop, allowing model training on distributed clusters.

// Example of a simple neural network in Deeplearning4jMultiLayerConfiguration config = new NeuralNetConfiguration.Builder()    .seed(123)    .updater(new Adam(0.001))    .list()    .layer(new DenseLayer.Builder()        .nIn(784)        .nOut(256)        .activation(Activation.RELU)        .build())    .layer(new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)        .nIn(256)        .nOut(10)        .activation(Activation.SOFTMAX)        .build())    .build();

MultiLayerNetwork model = new MultiLayerNetwork(config);model.init();model.fit(trainingData);

Smile (Statistical Machine Intelligence & Learning Engine)

Smile is a lightweight library covering a wide range of algorithms: from linear regression to random forest and SVM. It is written in pure Java and does not require additional native libraries, making it ideal for embedding into existing Java applications.

// Example of classification using Random Forest in Smileimport smile.classification.RandomForest;

double[][] X = {{1.0, 2.0}, {2.0, 3.0}, {3.0, 4.0}};int[] y = {0, 0, 1};

RandomForest model = RandomForest.fit(X, y);int prediction = model.predict(new double[]{2.5, 3.5});

Weka

Weka is a classic data mining tool that has been around for over 20 years. It provides a graphical interface and a Java API for dozens of algorithms: from simple Naive Bayes to complex ensemble methods. Weka is great for learning and experimentation but falls short in performance on large data.

Apache Spark MLlib

If your data does not fit into the memory of a single node, Spark MLlib is the standard choice. The library runs on top of Spark and supports distributed training. However, its Java API is less concise than its Scala or Python counterparts.

Advantages of Java in ML Tasks

Despite Python's dominance, Java has a number of objective advantages, especially in the corporate environment.

• Performance and Scalability. Java applications run faster than Python scripts due to JIT compilation and strict typing. For production systems where inference speed is critical, Java often wins.
• Ecosystem Integration. If your company uses Spring Boot, Kafka, Hadoop, or Cassandra, deploying an ML model in Java will not require rewriting the entire infrastructure. The model can be deployed as a regular microservice.
• Parallelism. Native support for multithreading and thread pools in Java simplifies processing large volumes of data and parallel model training.
• Static Typing. E