Machine Learning is becoming more popular and the majority of new mobile are AI-driven. It’s very tempting to integrate ML in your project but before making this decision it’s good to be prepared for this exciting journey. Managing ML-powered projects requires a different approach than in standard software projects. I’ve learned this the hard way while managing R&D projects in Netguru and taking care of 4 different apps. My first one - CarLens - was the most challenging project I’ve ever worked on as a Product Owner. We wanted to try out a completely new approach in R&D department and we had no clue how difficult it was going to be. We struggled for 7 months to craft and understand the model, create beautiful designs, and combine everything into a working app. We’ve learned how important a quality data set is and how important is also the way it’s being managed. All the learnings from the first project I implemented with success in the next ones. If you would like to hear the story of combining Machine Learning with Mobile, to learn some tips for working with the uncertainty of ML projects, and how one simple decision can influence the future of the whole project – this is a talk for you.

1. Managing mobile projects
powered with Machine
Learning
P. 01
INTRO TO

2. • Mobile Deputy Engineering Manager
• Product Owner of CarLens and BabyGuard
Krzysztof
Jackowski
WHO
ABOUT US

3. ABOUT US
R&D department
CarLens BabyGuard Portal AR

5. CarLens
App demo
url: https://youtu.be/sl5Zyqu0HSY

8. Portal AR
App demo
url: https://youtu.be/RWMs8AB1Q_k

9. Portal AR will be launched soon!
Sign up on:
https://www.producthunt.com/upcoming/portal-ar

10. Data! Data everywhere!
Data annotation
Performance metrics
Non-deterministic approach
Development process
ML Management
Table of contents

11. 12 GB
18 GB
Additional
20GB

12. Data is everywhere!
Data set
A versioned data portal for AWS
aka Github for data

13. Data! Data everywhere!
Data annotation
Performance metrics
Non-deterministic approach
Development process
ML Management
Table of contents

14. Usually your data is a
mess and you need to
organize it for analysis.
Data is everywhere!
Data Annotation

15. Binary classification Multiclass
classification
Multiclass-Multilabel
classification

17. Data! Data everywhere!
Data annotation
Performance metrics
Non-deterministic approach
Development process
ML Management
Table of contents

18. ML is all about math and
statistics. That’s why you
need proper metrics to
analyze the result
Performance metrics
Pure Math

19. Most popular indicators
Precision – what fraction of positive recognitions is really
positive
Recal – what fraction of positive values was detected by
the model
Performance metrics
Pure Math

20. Product indicators
fps – in Portal AR, how many frames are augmented per
second -> how smooth is the video
Video stability – how stable is the image augmentation,
noise percentage
Performance metrics
Pure Math

21. Data! Data everywhere!
Data annotation
Performance metrics
Non-deterministic approach
Development process
ML Management
Table of contents

22. ML
Non-deterministic approach
Machine learning is
stochastic, not deterministic.

23. How long it takes to fix a bug in
mobile app?
~Dev’s estimations + 20%
ML
Non-deterministic approach

24. How long it takes to improve a ML
model?
“Let me do some experiments and
we’ll see. Maybe a day, maybe a
month.”
ML
Non-deterministic approach

25. Data! Data everywhere!
Data annotation
Performance metrics
Non-deterministic approach
Development process
ML Management
Table of contents

27. Data
Code
ML-Ops
Process
Test
BuildTrain Deploy

28. Feedback

29. THANK YOU!
Let’s stay in contact
Krzysztof Jackowski
• krzysztof.jackowski@netguru.com
• twitter.com/kris_jac
• linkedin.com/in/krisjackowski