In this presentation, I show through case studies what are things one need to be careful with when building ML products.

1. Successful Adoption of Machine
Learning
Rudradeb Mitra | http://www.linkedin.com/in/mitrar/

2. Brief Bio
• 2002: Published first research paper on AI in an International conference.
• 2003-2009: Worked in Germany, Belgium and Scotland at Research Labs,
Universities and Startups on AI/ML.
• 2010: Graduated from University of Cambridge, UK
• 2010-2017: Built 6 startups.
• 2017-: Writer. Product Mentor of Google Launchpad. Democratization and
Decentralization of building ML products.

3. What is Machine Learning?
• Learning: Algorithms that can find patterns in past data and predict future patterns.
• Three kinds of Learning: Supervised, Unsupervised and Reinforcement.

4. How to build successful Machine Learning products?

5. Step I
• Select the right problem to solve

6. How to select the right problem?
"Stop identifying cats and start creating value"
• Bayesian error (Lowest possible error) rate is >80%
• Bayesian error rate is <20%

7. @copyright: Rudradeb Mitra

8. Next steps
• Selecting the right approach (intuitive or abstract thinking)
• Collecting the data (adoption)
• Selecting the right algorithm
• Building the product (including training and testing the data).

9. Three class of problems
• Solving problems that were thought unsolvable
• Solving problems that were thought not a problem
• Improving upon existing systems (error rate >70%)

10. Problem 1: Improving upon an existing system
Case study: Better risk premiums for young drivers

11. • Young drivers have high premiums so insurance companies fight
it difficult to attract new customers.
The problem

12. In partnership with:

13. Next steps
• Selecting the right approach: "If we can know how someone is driving then we can
calculate better risk"
• Collecting the data: How do we get users driving data?
• Selecting the right algorithm
• Building the product (including training and testing the data)

14. Collecting the data

15. Driver’s app
Record a trip Trip feedback

16. Goals & challenges Rewards
1. Provide incentives

17. 2: Cannot force to adopt and let users be in control
vs

18. • How?
3. Educate your customers

19. 4. Create a community

20. Results

21. What Machine Learning Algorithm to use

22. Data but ...
• Do not know who is a good or a a bad driver as we do not have labeled data.

23. Unsupervised learning
Picture taken from: http://www.ai-junkie.com/ann/som/som1.html
Find patterns in data

24. Problem 2: Problems that were thought unsolvable
Case study: Decentralized energy via Solar rooftop

25. • Solar adoption is low as the sales process is like 1960s vacuum
cleaner sale process.
The problem

27. Next steps
• Selecting the right approach: "If we can know how remotely find rooftops of the
people and create a simulator"
• Collecting the data: "Use solar satellite images" (public data)
• Making the algorithm: "From solar images to calculating rooftop energy potential".
• Building the product (including training and testing the data)

28. What we want?

29. But in reality...
In Germany and
most of Western world
In India

30. And google object detection does not work...

31. Plus the problem is slightly more complicated with
obstacles
Water tanks
Turbo
ventilator
Mumpty

32. •Type of obstacle in rooftop - We have identified 5-6
types of obstacles.
•Edges of the roof - We want to train a machine to learn
to identify the edges.
•Type of roof
Machine Learning to the rescue

33. Supervised learning

34. What algorithm to use?
Open source code and community!

35. Problem 3: Problems that were not a problem
Case study: Loans to people without bank account

36. • 70% of people in Vietnam don't have a bank account.
The problem

37. Next steps
• Selecting the right approach: "How can we predict future behavior?"
• Collecting the data: "Why would users give data?" (because want to get loans)
• Making the algorithm
• Building the product (including training and testing the data)

38. Future behavior of income earnings
• Education level
• Family background
• Current address
• Current job and salary

39. Unsupervised learning
Picture taken from: http://www.ai-junkie.com/ann/som/som1.html
Find patterns in data

40. Summarizing it all
• Select the right problem.
• Select the right approach through intuitive thinking.
• Collect data via incentivizing users to share data, do not get data behind their
backs.
• Select the right algorithm(s).

41. Key challenge in Machine Learning adoption
How do you get data and make users adopt?

42. Machine Learning is NOT rocket science
Adoption
How to collect data?
Abstract Thinking
Feel free to contact:
https://www.linkedin.com/in/mitrar/
mitra.rudradeb@gmail.com
Challenges are in
Algorithm
How to use deal with
incompleteness?
What data to collect?