Presentation from march meetup

1. iOS

6. Topics
1. AI Use Cases
2. Turi Create
3. Data for models
4. Training the models
5. Use in App

7. AI tasks
● Categorize items
● Similarity of items
● Match items to styles
CoreML could be utilized
● Categorize items
● Similarity of items*
AI Use Cases

8. Turi Create
Turi Create by Apple
https://github.com/apple/turicreate
Turi Create simplifies the development of custom machine learning models. You don't have to be a machine learning
expert to add recommendations, object detection, image classification, image similarity or activity classification to your app.
● Easy-to-use: Focus on tasks instead of algorithms
● Visual: Built-in, streaming visualizations to explore your data
● Flexible: Supports text, images, audio, video and sensor data
● Fast and Scalable: Work with large datasets on a single machine
● Ready To Deploy: Export models to Core ML for use in iOS, macOS, watchOS, and tvOS apps

9. Machine learning models:
Classifiers
Regression
Graph analytics
Clustering
Nearest Neighbors
Topic models
Turi Create
Common scenarios:
Recommender systems
Image classification
Image similarity
Object detection
Activity classifier
Text classifier

10. Data for models
● Where
○ https://github.com/zalandoresearch/fashion-mnist
○ Crawler
○ APIs
● Images
○ Size matters
● Metadata
○ Category
○ SKU
○ ...

11. Data for modelling

12. Data for modelling

13. Train the model

14. CoreML model

15. Use in App
https://developer.apple.com/documentation/coreml
Bundled model
+ Fast, ready to use
+ Simpler usage
- Bundle size can get huge
- Model update via new app version
Download during runtime
+ Bundle size is small
+ Model can be updated easily
- Can take time to download model

17. Bundled

18. Remote

19. Remote cont.

20. Remote cont.

21. Remote cont.

22. Demo

23. Thank you!
Jari Kalinainen
jari.kalinainen@zalando.fi
https://github.com/jvk75
https://www.linkedin.com/in/jarikalinainen/