Искусственный интеллект продолжает быть глобальным трендом. В данный момент он позволяет нам создавать приложения "с интеллектом", что открывает дополнительные возможности для разработчиков. Xamarin не имеет собственных AI или Machine Learning возможностей, но является важным инструментом, который позволяет использовать различные интеллектуальные системы, собирать данные и отображать их. В докладе мы рассмотрим некоторые сценарии использования AI/ML на платформе Xamarin.

1. Xamarin + AI

2. Agenda
1. Xamarin overview
2. AI for mobile platform
3. Cognitive Services overview
4. Demo

3. Anything you can do in Objective-C, Swift,
Java or Kotlin can be done in C# and Visual
Studio with Xamarin.

4. Native Performance

5. Always up-to-date
Same day support for new versions
Support for Apple Watch, KindleFire, Android Wear etc

6. Xamarin vs * iOS/Android vs Hybrid

7. Xamarin.Forms
Native UI for each platform
Platforms: iOS, Android, UWP, WPF, MacOS, Tizen, GTK#

8. Xamarin + Xamarin.Forms
Traditional Xamarin Approach With Xamarin.Forms:
More code-sharing, all native
iOS C# UI Windows C# UIAndroid C# UI
Shared C# Backend
Shared UI Code
Shared C# Backend

9. Why you should use Xamarin
• You are C# developer
• Reuse existing code
• Cross-platform
• Continue in Microsoft ecosystem

10. Why you should use Xamarin.Forms
• Fast POC
• Minimize costs for development for multiple
platforms:
 common interactions across device platforms
 code sharing is more important than custom UI
 little platform-specific features
• You are focusing on LOB apps
• You are not writing games
• You would like to partially reuse code from
WPF/UWP

11. AI for mobile platform
1. Consuming AI: Cognitive Services, ML Kit for
Firebase
2. AI on devices
• ONNX: open neural network exchange format
Cross-platform: Caffe2, TensorFlow, Core ML
• iOS: Core ML
• Android: TensorFlow

12. Microsoft AI

13. Pre-built RESTful APIs
Azure Cognitive Services
1. Search
2. Speech
3. Language
4. Knowledge
5. Vision

14. ONNX
Open neural network exchange format
Platforms: Caffe2, TensorFlow, Core ML etc
Create model and use on multiple platforms – no
vendor lock

15. iOS: Core ML
• Domains: Vision, NLP
• Built on Accelerate and
Metal
• Public model format
• Conversion tools

16. Android: TensorFlow
• Supports Android/iOS/Raspberry PI
• Lite version for mobile

17. Android: ML Kit for Firebase
• Supports: iOS/ Android
• Integrates with Google
Cloud Services
• Text recognition
• Face detection
• Barcode scanning
• Image labeling
• Landmark recognition

18. Demo

19. Links
• https://customvision.ai
• https://docs.microsoft.com/en-us/azure/cognitive-
services/custom-vision-service/home
• https://github.com/jimbobbennett/Xam.Plugins.OnDeviceC
ustomVision
• https://blogs.technet.microsoft.com/machinelearning/2018
/04/03/intelligent-edge-building-a-skin-cancer-prediction-
app-with-azure-machine-learning-coreml-xamarin/
• https://github.com/Microsoft/Mobile-Chest-X-Ray-Analysis

20. Q&A
mykhail.galushko@devrain.com