Kotlin for Machine Learning: Opportunities and Limitations for Beginners

Kotlin for Machine Learning: Opportunities and Limitations for Beginners

The world of machine learning (ML) has long been associated exclusively with Python. However, with the growing popularity of Kotlin in Android development and backend systems, many developers are asking: can Kotlin be used for machine learning? In this article, we will explore what tools and libraries are available for ML in Kotlin, its strengths and weaknesses, and whether a beginner should start their Data Science journey with this language.

Why Kotlin Might Be Interesting for ML?

Kotlin is a statically typed language that runs on the JVM. It is fully compatible with Java, which opens access to a vast ecosystem of Java libraries for machine learning. The main advantages of Kotlin for ML beginners are:

• Type Safety — Null Safety and strong typing reduce the number of runtime errors.
• Concise Syntax — Kotlin code is on average 30-40% shorter than Java code.
• Full Java Compatibility — you can use any Java libraries: Weka, Deeplearning4j, Smile, Apache Mahout.
• Multiplatform — Kotlin Multiplatform Mobile allows running ML models on iOS, Android, and the server.

For developers already familiar with Kotlin, this is a great opportunity to enter ML without learning a new syntax.

Main Libraries for ML in Kotlin

Let's look at the key tools that make Kotlin suitable for machine learning.

1. KotlinDL (Deep Learning)

This is the official library from JetBrains for deep learning. It is built on top of TensorFlow and allows creating neural networks in pure Kotlin.

// Example: creating a simple neural network for classificationimport org.jetbrains.kotlinx.dl.api.core.Sequentialimport org.jetbrains.kotlinx.dl.api.core.layer.core.Denseimport org.jetbrains.kotlinx.dl.api.core.layer.reshaping.Flattenimport org.jetbrains.kotlinx.dl.dataset.mnist

fun main() {    val model = Sequential.of(        Flatten(inputShape = longArrayOf(28, 28)),        Dense(128, activation = "relu"),        Dense(10, activation = "softmax")    )    model.compile(optimizer = "adam", loss = "sparse_categorical_crossentropy", metrics = listOf("accuracy"))        val (train, test) = mnist()    model.fit(train, epochs = 5, batchSize = 32)    model.evaluate(test).accuracy.also { println("Accuracy: $it") }}

The library is actively developed, supports ONNX and pre-trained models.

2. Smile (Statistical Machine Intelligence & Learning Engine)

Smile is a powerful framework for classical ML, written in Java. Thanks to compatibility, it works great in Kotlin.

// Example: linear regression with Smileimport smile.regression.OLSimport smile.data.data

fun main() {    val x = arrayOf(        doubleArrayOf(1.0, 2.0),        doubleArrayOf(2.0, 3.0),        doubleArrayOf(3.0, 4.0),        doubleArrayOf(4.0, 5.0)    )    val y = doubleArrayOf(3.0, 5.0, 7.0, 9.0)        val model = OLS.fit(x, y)    println("Coefficients: ${model.coefficients().contentToString()}")    println("Prediction for [5, 6]: ${model.predict(doubleArrayOf(5.0, 6.0))}")}

3. Deeplearning4j

Another popular Java library for deep learning that can be used from Kotlin. It supports GPU, distributed training, and importing models from Keras.

4. Multik

A library for working with multidimensional arrays (analogous to NumPy). It is necessary for efficient computations in ML.

// Example of working with Multik arraysimport org.jetbrains.kotlinx.multik.api.mkimport org.jetbrains.kotlinx.multik.ndarray.data.D2

fun main() {    val a = mk.ndarray(mk[1, 2, 3, 4])    val