Introducing Core ML

Core ML was first presented at Apple WWDC 2017. Defining Core ML as machine learning framework is not fair, because it lacks learning capabilities; it's rather a set of conversion scripts to plug the pre-trained model into your Apple applications. Still, it is an easy way for newcomers to start running their first models on iOS.

Core ML features

Here is a list of Core ML features:

• coremltools Python package includes several converters for popular machine learning frameworks: scikit-learn, Keras, Caffe, LIBSVM, and XGBoost.
• Core ML framework allows running inference (making predictions) on a device. Scikit-learn converter also supports some data transformation and model pipelining.
• Hardware acceleration (Accelerate framework and Metal under the hood).
• Supports iOS, macOS, tvOS, and watchOS.
• Automatic code generation for OOP-style interoperability with Swift.

The biggest Core ML limitation is that it doesn't support models training.

Exporting the model for iOS

In our Jupyter notebook...