Kotlin for Machine Learning: Capabilities and Limitations in Practice

Kotlin for Machine Learning: Capabilities and Limitations in Practice

The world of machine learning (ML) is traditionally associated with Python thanks to its rich ecosystem of libraries (TensorFlow, PyTorch, scikit-learn). However, in recent years, more and more developers are turning their attention to Kotlin — a modern, concise, and safe language running on top of the JVM. A natural question arises: can Kotlin be used for ML tasks, and if so, how effective is it? In this article, we will examine the real capabilities of Kotlin in the field of machine learning, provide practical code examples, and honestly discuss the limitations you will encounter.

Why might Kotlin be interesting for ML?

Kotlin was originally created as a more convenient alternative to Java, but its features (null safety, extension functions, coroutines) make it attractive for Data Science as well. Here are the key advantages:

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• Interoperability with Java: You can use any Java library for ML (e.g., DeepLearning4J, Smile, Apache Spark MLlib) directly from Kotlin.
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• Concise syntax: Kotlin code is often shorter and more readable than Java, which speeds up prototype development.
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• Type safety: Null-safety and strong typing reduce the number of runtime errors, which is critical for complex pipelines.
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• JVM performance: Kotlin compiles to JVM bytecode, which is well-optimized for long-running computational tasks.
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However, don't think that Kotlin is a "silver bullet." Let's move on to practice and see what real work looks like.

Practice: Libraries and Code Examples

1. Koma — A Pure Kotlin Library for Linear Algebra

Koma is a library written in Kotlin that provides basic capabilities for working with matrices and vectors. It is suitable for small tasks where a full ML framework is not required.

Example: Linear Regression using Koma

import koma.*\nimport koma.extensions.*\n\nfun main() {\n    // Training data: x -> y = 2*x + 1 + noise\n    val x = mat[1.0, 2.0, 3.0, 4.0, 5.0]\n    val y = mat[2.9, 5.1, 7.2, 8.8, 11.0]\n\n    // Add a column of ones for the bias term\n    val X = mat[ones(5, 1), x.T]\n\n    // Solve the normal equation: theta = (X^T * X)^{-1} * X^T * y\n    val theta = (X.T * X).inv() * X.T * y\n\n    println("Coefficients: a = ${theta[1,0]}, b = ${theta[0,0]}")\n    // Output: a ≈ 2.01, b ≈ 0.88\n}

Koma is simple, but its functionality is limited. For serious tasks, you will need more powerful tools.

2. Smile — A Full-Featured ML Library for the JVM

Smile (Statistical Machine Intelligence & Learning Engine) is one of the most powerful Java libraries for ML, which works excellently from Kotlin. It supports classification, regression, clustering, dimensionality reduction, and much more.

Example: Classification using Random Forest in Kotlin

import smile.classification.RandomForest\nimport smile.data.DataFrame\nimport smile.data.vector.DoubleVector\nimport smile.data.vector.StringVector\n\nfun main() {\n    // Load data (Fisher's Iris example)\n    val data = DataFrame.of(\n        DoubleVector.of("sepal_length", 5.1, 4.9, 6.2, 5.9),\n        DoubleVector.of("sepal_width", 3.5, 3.0, 3.4, 3.0),\n        DoubleVector.of("petal_length", 1.4, 1.4, 5.4, 5.1),\n        StringVector.of("species", "setosa", "setosa", "virginica", "virginica")\n    )\n\n    val features = data.select("sepal_length", "sepal_width", "petal_length")\n    val labels = data.stringVector("species").toArray()\n\n    // Train a Random Forest with 50 trees\n    val model = RandomForest.fit(features.toArray(), labels, 50)\n\n    // Prediction for a new sample\n    val prediction =