Take advantage of Core ML 2, the machine learning framework used across Apple products, including Siri, Camera, and QuickType. Core ML 2 delivers blazingly fast performance with easy integration of machine learning models, enabling you to build apps with intelligent features using just a few lines of code. And now, you can create your own models on Mac using Create ML and playgrounds in Xcode 10.
Core ML 2 lets you integrate a broad variety of machine learning model types into your app. In addition to supporting extensive deep learning with over 30 layer types, it also supports standard models such as tree ensembles, SVMs, and generalized linear models. Because it’s built on top of low level technologies like Metal and Accelerate, Core ML seamlessly takes advantage of the CPU and GPU to provide maximum performance and efficiency. You can run machine learning models on the device so data doesn’t need to leave the device to be analyzed.
You can easily build computer vision machine learning features into your app. Supported features include face tracking, face detection, landmarks, text detection, rectangle detection, barcode detection, object tracking, and image registration.
The Natural Language framework is a new framework you use to analyze natural language text and deduce its language-specific metadata. You can use this framework with Create ML to train and deploy custom NLP models.
This new framework lets you easily build machine learning models, with no machine learning expertise required. Familiar and easy to use thanks to Swift, Create ML is integrated into playgrounds in Xcode 10 so you can view model creation workflows in real time. Just add a few lines of Swift code to leverage our Vision and Natural Language technologies and create models optimized for the Apple ecosystem for a variety of tasks, including regression, image classification, word tagging, and sentence classification. And you can use your Mac to train models from Apple with your custom data — no need for a dedicated server.
Find tools to build models, easily convert models, or take advantage of ready-to-use Core ML models.
Read posts written by Apple engineers about their work using machine learning technologies.