This document discusses adding machine learning capabilities to iOS apps using Core ML. It introduces Core ML, describing it as Apple's framework for running machine learning models on iOS and OS X. It notes that Core ML allows inference of models but not training. The document also provides examples of converting pre-trained models to the Core ML format and integrating Core ML models into iOS apps. It demonstrates these concepts through examples of face detection and image classification models.

1. Add Machine Learning to your iOS
App with Core ML
Jean-Luc David
iOS Developer

2. My iOS Machine Learning App Circa 2012

3. OpenCV

4. Popular Perceptions

5. Popular Perceptions

6. Popular Perceptions

7. Nomenclature

8. Application of Machine Learning

9. Silicon Valley - Jian-Yang's Not Hotdog App

10. Fiction Becomes Reality

11. Types of Machine Learning

12. Core ML
• Announced at WWDC 17.
• Works only on iOS 11
• Inference engine
• Does not support training models.
• Heterogenous compute architecture - CPU & GPU.
• Open Source Python coremltool for converting
to coremlmodel file.

13. But I want to train models!

14. Runs On Device
User Privacy Data Cost Server Cost Available

15. How Does a Model Work?

16. Machine Learning Model Types

17. Demo - Face Detection & Recognition

18. Types of Machine Learning
Offline
+ Labels

19. Task - Find a Rose

20. Inference is Difficult

21. Inference is Difficult

22. Core ML Model
• Single Document
• Public Format
• Reduced Size
• Improved Accuracy
• Decreased Prediction
Time

23. Core ML Architecture
• Unified Fine Tuned Inference Engine
• Xcode Integration
• Built on Accelerate and Metal
• Public Model Format
• Support for Popular Training Libraries

24. Core ML Model
• Function learned from data
• Observed inputs
• Predicts outputs
• Single Document
• Public Format
• Ready to Use
• Task Specific

25. Sample Models
https://developer.apple.com/machine-learning/

26. Converting to Core ML
Core ML
Tools
Open Source

27. Converting to Core ML
• Download a trained .caffemodel and
a .prototxt of the dataset, as well as a .txt list
of names related to the model.
• Install Python 2.7 and pip
• Install, create, then activate the virtualenv.
• Write and run a Python script to convert
the .caffemodel to a Core ML model,
using coremltools
• Add the generated .mlmodel to the Xcode project

28. Demo - Converting a Model to MLModel

29. Xcode Integration

30. Swift ML Code
Generates this code

31. Model as Code
• Quick Initialization
• Optimized Prediction
Xcode Your App

32. Demo - FlowerClassifier

33. Resources
• https://www.slideshare.net/jldavid
• https://github.com/jldavid/FaceDetection
• https://github.com/jldavid/FlowerClassifier

34. Thank You