Kotlin for Machine Learning: Capabilities and Limitations

Online Python Trainer for Beginners

Learn Python easily without overwhelming theory. Solve practical tasks with automatic checking, get hints in Russian, and write code directly in your browser — no installation required.

Start Course

Introduction: Why Kotlin in Machine Learning?

The world of machine learning (ML) and deep learning (DL) has long been the domain of Python. However, with the growing popularity of Kotlin, especially in Android development and backend, a logical question arose: can this modern, concise, and safe language be used for Data Science tasks? The answer is yes, it can. Kotlin offers a number of unique advantages, such as full compatibility with Java (and therefore with the JVM ecosystem), null safety, functional capabilities, and high performance.

In this article, we will break down the key libraries for machine learning in Kotlin, look at practical examples, and also honestly discuss the limitations you may encounter. The article will be useful both for Kotlin developers wanting to enter ML and for data scientists looking for alternatives to Python.

Main Libraries for ML in Kotlin

The Kotlin ecosystem for ML is actively developing. Although it is not as extensive as Python's, it already includes powerful tools.

KotlinDL: Deep Learning "Out of the Box"

KotlinDL is arguably the main library for deep learning in pure Kotlin, developed by JetBrains. It provides a high-level API for building, training, and deploying neural networks.

Advantages:

Written in Kotlin, ensuring perfect integration with the language.
Supports loading pre-trained models (e.g., VGG-16, ResNet) from TensorFlow and ONNX.
Has a simple DSL for building models.

Example: Creating a simple neural network for classification

import org.jetbrains.kotlinx.dl.api.core.Sequentialimport org.jetbrains.kotlinx.dl.api.core.layer.*import org.jetbrains.kotlinx.dl.api.core.optimizer.Adamimport org.jetbrains.kotlinx.dl.dataset.mnist

fun main() {    val model = Sequential.of(        Flatten(inputShape = longArrayOf(28, 28)),        Dense(outputSize = 128, activation = Activations.Relu),        Dense(outputSize = 10, activation = Activations.Softmax)    )

    model.use {        it.compile(optimizer = Adam(), loss = Losses.SOFT_MAX_CROSS_ENTROPY_WITH_LOGITS)        it.fit(dataset = mnist.train, epochs = 5, batchSize = 32)        val accuracy = it.evaluate(dataset = mnist.test).accuracy        println("Accuracy: $accuracy")    }}

KMath: Scientific Computing

The KMath (Kotlin Mathematics) library provides powerful tools for linear algebra, statistics, optimization, and other mathematical operations needed in ML. It is an analogue of NumPy in the Kotlin world.

Example: Working with tensors

import space.kscience.kmath.tensors.core.DoubleTensorAlgebraimport space.kscience.kmath.tensors.core.tensorAlgebra

fun main() {    val tensorA = DoubleTensorAlgebra.fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))    val tensorB = DoubleTensorAlgebra.fromArray(intArrayOf(2, 3), doubleArrayOf(6.0, 5.0, 4.0, 3.0, 2.0, 1.0))

    val sum = DoubleTensorAlgebra { tensorA + tensorB }    println("Sum of tensors: ${sum.toList()}")}

Smile and other JVM Libraries

Thanks to compatibility with Java, Kotlin can use all Java libraries for ML, such as Smile (Statistical Machine Intelligence & Learning Engine), DeepLearning4j, and Weka. This provides access to a huge number of algorithms, from decision trees to clustering.