Mastering Machine Learning in iOS with SwiftUI: From Basics to Advanced
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- Discussions
- Introduction
- Chapter 1: Introduction to Machine Learning and Its Relationship to AI
- Chapter 2: Machine Learning Techniques
- Chapter 3: Apple’s Machine Learning Ecosystem
- Chapter 4: Create ML and Core ML
- Chapter 5: CreateML and Transfer Learning
- Chapter 6: Getting Started with Create ML
- Chapter 7: VisionFeaturePrint_Screen
- Chapter 8: Create ML Project Interface and Model Training
- Chapter 9: Improving Model Performance
- Chapter 10: Vision Framework with Core ML
- Chapter 11: Using Machine Learning Model with Core ML
- Chapter 13: Using Existing ML Model in Apps
- Chapter 12: Steps to Train Machine Learning Models
- Chapter 14: Object Detection in Create ML - Data Prep
- Chapter 15: Object Detection in Create ML - Model Training
- Chapter 16: Object Detection in Create ML - Using Model
- Chapter 17: Style Transfer Technique
- Chapter 18: Using Style Transfer Technique in SwiftUI App
- Chapter 19: Hand Pose Classification
- Chapter 20: Rock-Paper-Scissor App
- Chapter 21: Action Classification Model using Create ML
- Chapter 22: Using Action Classifier Model in an App
- Chapter 23: Activity Classification using Create ML
- Chapter 24: Using Activity Classification Model in SwiftUI App
- Chapter 25: Collecting Activity Motion Data using iPhone
- Chapter 26: Sound Classification in Create ML
- Chapter 27: Animal Sound Classification in SwiftUI
- Chapter 28: Built-in Sound Classification with Sound Analysis framework
- Chapter 29: Text Classification in Create ML
- Chapter 30: Word Tagging in Create ML
- Chapter 31: Tabular Classification
- Chapter 32: Tabular Regression
- Chapter 33: Recommendations Model using Create ML
- Chapter 34: Understanding Training Data
- Chapter 35: Thank You for Reading!

Chapter 4: Create ML and Core ML

Create ML

Create ML is Apple’s machine learning framework, designed to make the process of building and training custom ML models more accessible to developers. It offers a user-friendly interface that simplifies model creation, making it an excellent tool for those who may not have extensive machine learning expertise.

With Create ML, developers can train models using their own datasets without needing to write complex ML algorithms. The framework supports a variety of machine learning tasks, including:

Image Classification – Training models to recognize and categorize images.
Object Detection – Identifying and locating objects within an image.
Text Analysis – Processing natural language to perform tasks such as sentiment analysis and text classification.
Sound Classification – Recognizing and categorizing audio inputs.
Tabular Data Processing – Analyzing structured datasets for predictive modeling.

Since Create ML is optimized for Apple’s ecosystem, it seamlessly integrates with Core ML, allowing trained models to be deployed directly into iOS, macOS, watchOS, VisionOS and tvOS applications. Additionally, the framework enables on-device training and inference, ensuring that data processing remains private and efficient without requiring cloud-based computation.

By leveraging Create ML, we can customize machine learning models to meet our app’s specific needs, enabling AI-driven features while maintaining high performance and user privacy.

Create ML is tightly integrated with Xcode, Apple’s primary development environment, providing a streamlined and intuitive approach to building machine learning models. It offers developers two powerful ways to train and evaluate models:

Create ML Graphical Interface (GUI)

The Create ML app provides a visual, drag-and-drop interface for training machine learning models without writing extensive code. Developers can import datasets, configure training parameters, visualize results, and fine-tune models with ease. This no-code approach is ideal for beginners or those who want a quick and efficient way to create ML models tailored to their app’s needs.

Image from Apple.com

Swift-based API for Programmatic Development

For developers who prefer more control and flexibility, Create ML offers a Swift-based API that enables programmatic model training within Xcode.

Using Swift, developers can automate dataset preparation, model training, validation, and performance evaluation, allowing for more customization and scalability. This method is particularly useful for integrating machine learning into complex applications or automating training workflows.

By offering both a user-friendly graphical tool and a Swift-based API, Create ML ensures that developers—regardless of their machine learning expertise—can efficiently build and deploy powerful AI-driven features within Apple’s ecosystem.

Create ML comprises three components: the Create ML app, the Create ML Framework, and the Create ML Components. The Create ML app enables users to train models with a simple click of a button. It is built upon the Create ML Framework. If you’re interested in automating the model creation process or enhancing the on-device personalizationexperience, you can directly use the Create ML Framework.

Under the hood, Create ML utilizes system domain frameworks such as Vision, Natural Language, and Sound Analysis to customize machine learning models to your specific use case and machine learning data.

Credit Apple.com

Core ML

Core ML is Apple’s machine learning framework, designed to seamlessly integrate ML models into applications across iOS, macOS, iPadOS, visionOS, watchOS, and tvOS. It empowers developers to incorporate intelligent features into their apps without requiring extensive knowledge of machine learning.

Image from Apple.com

Benefits of using Core ML

Cross-Platform Compatibility: Core ML allows developers to deploy machine learning models across all Apple devices, ensuring a consistent user experience whether on an iPhone, iPad, Mac, Apple Watch, Apple TV, or Vision Pro headset.
Support for Pre-Trained Models: Developers can leverage pre-trained ML models from Create ML, TensorFlow, PyTorch, ONNX, and other ML platforms to integrate AI-driven features into their apps quickly. These models can perform tasks like image recognition, object detection, speech analysis, text processing, recommendation systems, and predictive analytics.
Optimized for On-Device Performance: Core ML is specifically designed to run machine learning models directly on the device, eliminating the need for cloud-based computation. This ensures low latency and high-speed execution, making features like real-time image recognition and voice processing more efficient.
Enhanced User Privacy: Since Core ML processes data locally on the device, sensitive user information never leaves the device. This makes Core ML an excellent choice for apps that require AI-driven personalization while maintaining high standards of security and data privacy.
Energy Efficiency: Core ML is optimized to use the hardware acceleration capabilities of Apple’s A-series, M-series, and Neural Engine chips, ensuring efficient power consumption while running ML models. This makes it possible to deploy powerful machine learning capabilities without significantly impacting battery life.

Create ML and Core ML are both Apple frameworks designed for working with machine learning on Apple platforms, but they serve different purposes and are used at different stages of the machine learning workflow.

Conclusion

In summary, Create ML is used for creating custom machine learning models, while Core ML is used for integrating those models into apps. Together, they provide a complete solution for developing ML-powered applications on Apple platforms.