The document discusses implementing mobile machine learning to determine a person's activity, such as walking, running, or cycling, using a structured approach involving data collection and model creation. It highlights the growth in artificial intelligence spending and details a typical framework companies use to develop AI solutions. Additionally, it addresses considerations for using mobile machine learning, such as user privacy and problem definition.

1. The Mobile Machine Learning
Extravaganza!
Darryl Bayliss
Team member at raywenderlich.com
Mobile Engineer at Axon Vibe

2. https://codelabs.developers.google.com/codelabs/tensorflow-lab1-helloworld
The Problem
How can we work out if a person is walking, running
or cycling?

3. https://codelabs.developers.google.com/codelabs/tensorflow-lab1-helloworld
Software Engineering

4. https://codelabs.developers.google.com/codelabs/tensorflow-lab1-helloworld
The Classic Solution

5. https://codelabs.developers.google.com/codelabs/tensorflow-lab1-helloworld
Machine Learning

6. The Machine Learning Solution
https://codelabs.developers.google.com/codelabs/tensorflow-lab1-helloworld

7. https://codelabs.developers.google.com/codelabs/tensorflow-lab1-helloworld
Using The Model

8. 1. Find your data
2. Structure and label your data
3. Create a model using a machine learning program
4. Use the model to make predictions
Machine Learning Checklist

9. Demo Time!

10. – International Data Corporation Report on Artificial Intelligence spending
“Worldwide spending on artificial intelligence (AI) systems is forecast
to reach $35.8 billion in 2019, an increase of 44.0% (from 23.8 billion)
over the amount spent in 2018.”
https://www.idc.com/getdoc.jsp?containerId=prUS44911419
Artificial Intelligence Spending

11. – Deloitte Insights: Technology, Media, and Telecommunications Predictions 2019
“To develop their AI services, they’re following a familiar playbook:
(1) find a solution to an internal challenge or opportunity;
(2) perfect the solution at scale within the company;
(3) launch a service that quickly attracts mass adoption.”
https://www2.deloitte.com/content/dam/insights/us/articles/TMT-Predictions_2019/DI_TMT-predictions_2019.pdf
Innovation in Artificial Intelligence

13. Federated Learning
https://ai.googleblog.com/2017/04/federated-learning-collaborative.html

14. Federated Learning
https://ai.googleblog.com/2017/04/federated-learning-collaborative.html

15. https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
Federated Learning

16. Should You Use Mobile Machine Learning?
• Is user privacy a concern for your model?
• Is the problem well defined?
• Does your model have well defined inputs and outputs?
• Is an approximate answer acceptable?
https://medium.com/better-programming/should-you-use-machine-learning-73a7746f7280

17. Learn more about
Mobile Machine
Learning

18. Learn to build
Android Apps

19. Follow us on
Twitter
@axonvibe

20. Thank you :)