Core ML

Core ML is Apple's machine learning framework for integrating trained models into iOS, macOS, watchOS, and tvOS applications. It provides a unified interface for running models on-device, leveraging hardware acceleration like the Neural Engine for efficient inference. The framework supports various model types, including neural networks, tree ensembles, and support vector machines, with tools for converting models from popular formats.

Also known as: CoreML, Apple Core ML, Core Machine Learning, iOS ML, macOS ML

Why learn Core ML?

Developers should learn Core ML when building Apple ecosystem apps that require on-device machine learning capabilities, such as image recognition, natural language processing, or predictive analytics, to ensure privacy, low latency, and offline functionality. It's essential for iOS/macOS developers aiming to incorporate AI features without relying on cloud services, benefiting from Apple's hardware optimizations and seamless integration with Swift and other Apple frameworks.

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
• book: Core ML Book: 'Core ML Survival Guide'