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.
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.