Swift (iOS/macOS) for Data Science: From Theory to Production

The world of Data Science is traditionally associated with Python and R, but the Apple ecosystem is rapidly changing the rules of the game. The book "Swift (iOS/macOS) for Data Science" is the first practical guide that proves: Swift is a full-fledged tool for building machine learning models, processing data, and deploying AI solutions in native applications.

You will learn how to use the full power of Apple frameworks (Core ML, Create ML, Tabular Data, Vision, Natural Language) to solve real-world problems — from sales prediction to computer vision. The book is structured on the "from simple to complex" principle: starting with the basics of working with data in Swift and ending with deploying ready-made models to the App Store.

Who this book is for:

• iOS/macOS developers who want to add ML features to their applications
• Data Scientists who want to use Swift for prototyping and production
• Technical students interested in machine learning
• Anyone who wants to understand how recommendation systems, image classifiers, and NLP work on Apple devices

What you will learn:

• How to load, clean, and visualize data using Swift and libraries (Swift Numerics, TabularData)
• Creating regression, classification, and clustering models with Create ML
• Core ML integration: converting models from Python (TensorFlow, PyTorch) to .mlpackage format
• Working with neural networks: training on GPU (Metal Performance Shaders) and optimization for mobile devices
• Advanced techniques: Transfer Learning, Federated Learning, On-Device Training
• Deployment: A/B testing of models, monitoring, and updating via Firebase/App Center
• Examples: a recommendation engine for streaming, an object detector for the camera, an NLP assistant with SiriKit

The book contains over 30 ready-made projects with source code that can be run immediately in Xcode. All examples are relevant for Swift 5.9+, iOS 17+, and macOS 14+. Special attention is paid to performance: you will learn how to reduce model sizes by 10 times without losing accuracy and process data in real time.

This is not just a translation of Apple documentation — it is a concentrate of practical experience gathered over years of developing ML products for the Apple ecosystem. You will get ready-made architectural solutions, production checklists, and battery optimization tips.

After reading the book, you will be able to independently create intelligent applications that run entirely on the device, without sending data to the server. Your users will get fast and private AI features — from smart search suggestions to real-time photo analysis.

Bonus: in the book you will find a chapter on career growth — how a Swift portfolio for Data Science can help you stand out among thousands of Python developers and get a job at top companies.