Machine Learning in Mobile Applications

Introduction

• Machine learning in mobile apps
• What you should know
• Using the exercise files

1. Introduction to Machine Learning

• What is machine learning?
• Required concepts
• Why does this matter for my app?
• Training a model
• Machine learning vs. deep learning
• What can I do with machine learning?
• Server-side vs. client-side ML
• ML frameworks

2. Server Models: IBM Watson

• Overview of Watson
• Natural Language Understanding: Set up
• Natural Language Understanding: Train the model
• Visual Recognition: Set up
• Visual Recognition: Train the model
• Create a custom model
• Train and deploy a custom model
• Install client SDK package
• Client tie to Natural Language
• Client tie to Visual Recognition call setup
• Client tie to Visual Recognition response
• Client tie to custom model: Get an access token
• Client tie to call custom model service
• Client tie to get custom model response
• Run the client app

3. Server Models: Azure Machine Learning

• Azure Machine Learning overview
• Language Understanding: Set up
• Language Understanding: Intents
• Language Understanding: Utterances
• Custom Vision: Set up
• Machine Learning Studio: Set up
• Machine Learning Studio: Create model
• Machine Learning Studio: Publish model
• Install client SDK package
• Client tie to LUIS
• Client tie to Custom Vision model
• Client tie to custom model
• Client tie to custom model: Set up request
• Client tie to custom model: Make the call
• Run the clent app

4. Client Models: Core ML

• Core ML overview
• Core ML: Create Natural Language model
• Core ML: Create Visual Recognition model
• Client tie to Natural Language model
• Client tie to Visual Recognition model
• Client tie to Visual Recognition: Converting model
• Run the client app

5. Understanding the Offerings

• Different philosopies of the vendors
• Why client-side model vs. server-side
• When to use one or the other of these solutions

Conclusion

• Next steps