Artificial Intelligence (AI) and Machine Learning (ML) are all the rage right now. In this session, we’ll be looking at engineering best practices that can be applied to ML, how ML research can be integrated with an agile development cycle, and how open ended research can be managed within project planning According to a recent Narrative Science survey, 38 per cent of enterprises surveyed were already using AI, with 62 per cent expecting to be using it by 2018. So it’s understandable that many companies might be feeling the pressure to invest in an AI strategy, before fully understanding what they are aiming to achieve, let alone how it might fit into a traditional engineering team or how they might get it to a production setting. At Basement Crowd we are currently taking a new product to market and trying to go from a simple idea to a production ML system. Along the way we have had to integrate open ended academic research tasks with our existing agile development process and project planning, as well as working out how to deliver the ML system to a production setting in a repeatable, robust way, with all the considerations expected from a normal software project.

1. From Theory
to Production
#TeamAwesome
Agile
Machine
Learning

2. Who am I?
CTO @ Basement Crowd
rob_hinds
robhinds

3. Agile Machine LearningAgile Machine Learning

4. Why should you care?

5. “Your AI will be a
key point of
distinction for
your business”
Accenture - Technology Visions 2017

6. “Products that don’t
use [AI or ML] will die a
natural death”
Manish Singhal - Forbes India

7. 62%
Percentage of organizations expecting to be using AI Technologies by 2018
Narrative Science - Outlook on Artificial Intelligence in the Enterprise 2016

8. https://spectrum.ieee.org/computing/software/the-2017-top-programming-languages

9. https://insights.stackoverflow.com/survey/2017

10. DON’T
BELIEVE
THE
HYPE

11. “The first wave of
corporate AI is
doomed to fail”
Harvard Business Review - The First Wave of Corporate AI Is Doomed to Fail

12. So, what can we do?

13. Sensible engineering &
product design
principles are the key

14. Product
Thinking
for Machine
Learning

15. https://www.useronboard.com/features-vs-benefits/
Machine Learning !=
Your Product

16. Is Machine Learning
part of your MVP?

17. Is your
Machine Learning
Mission Critical?

18. https://spectrum.ieee.org/automaton/robotics/artificial-intelligence/how-google-self-driving-car-works

19. Data
(photo)
Machine Learning
(suggested tags)
Curation
(creator)
Customers
(consumers)

20. 3 Principles:
1) Don’t build Machine Learning for the sake of it
2) Do you need ML in your MVP to test product
market fit?
3) Is your ML mission critical?

21. “a right to explanation”
GDPR: Article 22

22. Engineering Thinking
for Machine Learning

23. It’s still just
Engineering

24. Clean code
Testing
Modularity

25. ML Anti-Patterns:
Dead experiment code - Configuration debt
Code glue - Pipeline jungles
Sculley, D., et al. "Hidden technical debt in machine learning systems."

26. “Glue code and pipeline jungles are
symptomatic of integration issues that may
have a root cause in overly separated
‘research’ and ‘engineering’ roles”
Sculley, D., et al. "Hidden technical debt in machine learning systems."

27. Agile Thinking for
Machine Learning

28. Research sprints

29. Build > Measure > Learn
Eric Ries - The Lean Startup

30. Simple Rule based
Traditional off-the-shelf libraries
Deep Learning & Sophisticated ML pipelines

31. Agile Machine LearningAgile Machine Learning

32. Text ➡ Numbers

33. Text ➡ Numbers
AI
pretends
to
fail
Turing
Test.
3
145
82
31
96
733
Bag-of-Words
https://en.wikipedia.org/wiki/Bag-of-words_model

34. Text ➡ Numbers
AI
pretends
to
fail
Turing
Test.
[1.25,...,3.58]
[0.05,...,0.07]
[45.8,...,9.70]
[0.78,...,10.1]
[100.1,...,7.8]
[445.1,...,2.1]
word2vec
https://www.tensorflow.org/tutorials/word2vec

35. Demo

36. Theory to Production

38. Choosing your stack
Available skills set
Existing knowledge & tech stack
Hiring pool

39. Modern architecture &
cloud technology
makes ML deployment
easier

40. Photo by frank mckenna on Unsplash

41. https://pbs.twimg.com/media/C4vf8SQUcAALCyl.jpg

42. AWS
nginx
Zuul (Edge
server)
Eureka (service
registry)
Recommendation
Service
Movies Service

43. Take aways
● Approach it with the rigour and principles of any other
engineering product
● De-risk the cost of failure with sensible product
management
● Engineer sensibly!
● Use tried and tested build (CI) and deployment
approaches

44. Thanks!
(any questions?)

45. References
1. https://resources.narrativescience.com/Resources/Resource-Library/Article-Detail-Page/announcing
-our-new-research-report-outlook-on-artificial-intelligence-in-the-enterprise-2016
2. https://www.accenture.com/us-en/insight-disruptive-technology-trends-2017
3. http://fortune.com/2016/06/03/tech-ceos-artificial-intelligence
4. https://hbr.org/2017/04/the-first-wave-of-corporate-ai-is-doomed-to-fail
5. http://theleanstartup.com/principles
6. http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
7. https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf
8. Photos from unsplash.com