Use the Core ML API directly to support custom workflows and advanced use cases.


In most cases, you interact only with your model's dynamically generated interface, which is created by Xcode automatically when you add a model to your Xcode project. You can use Core ML APIs directly in cases where you need to support custom workflows or advanced use cases. As an example, if you need to make predictions while asynchronously collecting input data into a custom structure, you can use that structure to provide input features to your model by adopting the MLFeatureProvider protocol.



class MLModel

An encapsulation of all the details of your machine learning model.

Model Features

protocol MLFeatureProvider

An interface that represents a collection of feature values for a model.

class MLDictionaryFeatureProvider

A convenience wrapper for the given dictionary of data.

class MLFeatureValue

An immutable instance representing a feature's type and value.

class MLFeatureDescription

A description of a feature.

class MLMultiArray

A multidimensional array used as input or output for a model.


struct MLModelError

Error codes for Core ML.