Core ML

Core ML is Apple's machine learning framework for integrating trained models into iOS, macOS, watchOS, and tvOS apps. It provides a unified interface for running models on-device, leveraging hardware acceleration like the Neural Engine for efficient inference. Developers can use it to add features such as image recognition, natural language processing, and predictive analytics directly in their Apple ecosystem applications.

Also known as: CoreML, CoreML Framework, Apple Core ML, Core Machine Learning, CoreMLKit

Why learn Core ML?

Developers should learn Core ML when building apps for Apple platforms that require on-device machine learning capabilities, as it ensures privacy, low latency, and offline functionality. It is particularly useful for applications in areas like computer vision (e.g., AR filters), text analysis (e.g., sentiment detection), and personalized recommendations, where real-time processing without cloud dependency is critical.

Learning Resources

• docs: Core ML Documentation
• video: Introducing Core ML - WWDC Video
• tutorial: Core ML Tutorial on Ray Wenderlich
• course: Machine Learning with Core ML 2 and Swift Course on Udemy