Core ML is optimized for on-device performance of a broad variety of model types by leveraging Apple hardware and minimizing memory footprint and power consumption.
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Run models fully on-device
Core ML models run strictly on the user’s device and remove any need for a network connection, keeping your app responsive and your users’ data private.
Run advanced neural networks![]()
Core ML supports the latest models, such as cutting-edge neural networks designed to understand images, video, sound, and other rich media.
Deploy modelsNEW
With Core ML Model Deployment, you can easily distribute models to your app using CloudKit.
Convert models to Core ML
Models from libraries like TensorFlow or PyTorch can be converted to Core ML using Core ML Converters more easily than ever before.
Personalize models on-device![]()
Models bundled in apps can be updated with user data on-device, helping models stay relevant to user behavior without compromising privacy.
Encrypt modelsNEW
Xcode supports model encryption enabling additional security for your machine learning models.
Download The Latest Imagine Learning MachineCreate ML
Build and train Core ML models right on your Mac with no code.
Core ML Converters
Apple mac os x download. Convert models from third-party training libraries into Core ML using the coremltools Python package.
Models
Xunlei download for mac. Get started with models from the research community that have been converted to Core ML.
Powerful Apple SiliconDownload The Latest Imagine Learning Mac Os
Core ML is designed to seamlessly take advantage of powerful hardware technology including CPU, GPU, and Neural Engine, in the most efficient way in order to maximize performance while minimizing memory and power consumption.
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December 2020
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