Swift for Machine Learning: Capabilities and Limitations — Part 2

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Swift for Machine Learning: Capabilities and Limitations — Part 2

In the first part, we covered the basics of using Swift in the context of machine learning. Today we will dive deeper into practical aspects: we'll examine libraries, write real code examples, and discuss limitations that are important for everyone studying Swift for beginners. If you want to master programming in Swift with a focus on ML — this article is for you.

1. Main Swift Libraries for Machine Learning

The Swift ecosystem for ML is actively developing. Here are the key tools you should know:

Core ML — a framework from Apple for integrating ready-made models into iOS/macOS applications.
Create ML — a tool for training models directly on a Mac without deep ML knowledge.
TensorFlow Swift (outdated, but still useful for learning) — a port of the popular library.
Swift for TensorFlow — an experimental project that influenced modern approaches.

For learning how to work with these libraries, it is recommended to start with Create ML — it is intuitive even for beginners.

2. Code Example: Image Classification with Core ML

Let's look at a simple example of using a pre-trained MobileNetV2 model for image classification. This is an excellent task for Swift for beginners.

import CoreMLimport Visionimport UIKit

// Load the modelguard let model = try? VNCoreMLModel(for: MobileNetV2().model) else {    fatalError("Failed to load model")}

// Create a requestlet request = VNCoreMLRequest(model: model) { request, error in    guard let results = request.results as? [VNClassificationObservation],          let topResult = results.first else {        print("Failed to classify")        return    }    print("Object: \\(topResult.identifier), confidence: \\(topResult.confidence)")}

// Load the imagelet image = UIImage(named: "cat.jpg")!let handler = VNImageRequestHandler(cgImage: image.cgImage!, options: [:])try? handler.perform([request])

This code shows how easy it is to integrate ML into an application. Programming in Swift is becoming increasingly accessible thanks to such frameworks.

3. Limitations of Swift in ML

Despite the progress, Swift has drawbacks that are important to consider when learning and choosing a tool:

Small community — compared to Python, finding ready-made solutions is more difficult.
Limited GPU support — on macOS, model training runs slower than on Linux with CUDA.
Few libraries — there are no full equivalents of scikit-learn, PyTorch, or Keras.
Focus on iOS/macOS — Swift is not suitable for cross-platform ML projects.

However, these limitations are not critical if you are developing for the Apple ecosystem. For Swift for beginners, it is important to understand: the language is excellent for integration, but not for research.

4. Prospects of Swift in Machine Learning

Apple is actively investing in ML: improving Core ML, adding support for new architectures (e.g., transformers). Every year, programming in Swift becomes more attractive for ML tasks.

Recommendations for those who want to delve deeper into learning Swift for ML: