WWDC Notes

Core ML 3 Framework

Show Apple's description.
Core ML 3 now enables support for advanced model types that were never before available in on-device machine learning. Learn how model personalization brings amazing personalization opportunities to your app. Gain a deeper understanding of strategies for linking models and improvements to Core ML tools used for conversion of existing models.

On device model personalization

  • Previously only bundles in the app
  • Apple has intentionally avoided trying to load models from the cloud as this compromises privacy and scalability
  • Provide some new training examples and receive a new model
  • Demo uses pencil kit to capture input drawing and find a emoji. With the new API the user can train the model to understand the user’s way of drawing.
    • Import an editable CoreML model this model has some parameters for updating declared
    • CoreML generates some classes to work with these inputs
    • To update:
      • Load the model from the bundle
      • Prepare the training data
      • Create an MLUpdateTask which async produces a CoreML model
  • We can update the model in the background. iOS will give you up to several minutes (see notes on the background tasks framework)

In short, Core ML 3 lets us update the model entirely locally (ad-hoc for the given user).

What’s inside a MLmodel

Core ML 2Core ML 3

In CoreML 2 models consist mostly of:

  • Parameters: things like the weights of the layers, if it's a neural network for example
  • Metadata: licensing and authors
  • Interface: describes how our app can interact with this model.

What’s new on CoreML 3:

  • Update parameters: describes what parts of the model are updatable and how it's updatable.
  • Update Interface: model interface our app can leverage to make these updates happen.

Neural Networks

Neural networks are great at solving challenging task, such as understanding the content of an image or a document or an audio clip.

  • Core ML 3 has more advanced features available for Control flow in Neural Networks (e.g. Branching, Loops)
  • Internal improvements: Made existing layers more generic, control flow, add new mathematical operations

BERT in depth

BERT (Bidirectional Encoder Representation from Transformers) is state of the art machine learning model.

The BERT model is actually a neural network that can perform multiple tasks for natural language understanding.

Inside the BERT model there are a bunch of modules.

And inside these modules there are a bunch of layers.

(Models on the left, layers on the right)

In short this is what’s happening behind the scenes of the demo:

Other improvements

  • Linked Models: Shared models between pipelines.
  • Image Features: now images (from url, or CGImageSource) are supported for models rather than just pixel buffers.
  • MLModelConfiguration can set preferred MetalDevice (use full for multi GPU env) and an option to speed up the performance of the GPU calculations (at accuracy cost).

Missing anything? Corrections? Contributions are welcome 😃


Written by


Federico Zanetello

Federico Zanetello is an iOS Engineer with strong passion for Swift, minimalism, and design. When he’s not busy automating things, he can be found writing at fivestars.blog and/or playing with the latest shiny toys.