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From Theory
to Production
#TeamAwesome
Agile
Machine
Learning
Who are we?
Sumanas Sarma
@qaoqz
Rob Hinds
@rob_hinds
Agile Machine LearningAgile Machine Learning
Why should you care?
“Your AI will be a
key point of
distinction for
your business”
Accenture - Technology Visions 2017
“Products that don’t
use [AI or ML] will die a
natural death”
Manish Singhal - Forbes India
62%
Percentage of organizations expecting to be using AI Technologies by 2018
Narrative Science - Outlook on Artificial In...
https://spectrum.ieee.org/computing/software/the-2017-top-programming-languages
https://insights.stackoverflow.com/survey/2017
DON’T
BELIEVE
THE
HYPE
“The first wave of
corporate AI is
doomed to fail”
Harvard Business Review - The First Wave of Corporate AI Is Doomed to F...
So, what can we do?
Sensible engineering &
product design
principles are the key
Product
Thinking
for Machine
Learning
https://www.useronboard.com/features-vs-benefits/
Machine Learning !=
Your Product
Is Machine Learning
part of your MVP?
Is your
Machine Learning
Mission Critical?
https://spectrum.ieee.org/automaton/robotics/artificial-intelligence/how-google-self-driving-car-works
Data
(photo)
Machine Learning
(suggested tags)
Curation
(creator)
Customers
(consumers)
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?...
Engineering Thinking
for Machine Learning
It’s still just
Engineering
Clean code
Testing
Modularity
ML Anti-Patterns:
Dead experiment code - Configuration debt
Code glue - Pipeline jungles
Sculley, D., et al. "Hidden techn...
“Glue code and pipeline jungles are
symptomatic of integration issues that may
have a root cause in overly separated
‘rese...
Agile Thinking for
Machine Learning
Research sprints
Build > Measure > Learn
Eric Ries - The Lean Startup
Simple Rule based
Traditional off-the-shelf libraries
Deep Learning & Sophisticated ML pipelines
Agile Machine LearningAgile Machine Learning
Text ➡ Numbers
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...
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,......
Demo
Theory to Production
Choosing your stack
Available skills set
Existing knowledge & tech stack
Hiring pool
Modern architecture &
cloud technology
makes ML deployment
easier
https://pbs.twimg.com/media/C4vf8SQUcAALCyl.jpg
AWS
nginx
Zuul (Edge
server)
Eureka (service
registry)
Recommendation
Service
Movies Service
Thanks!
(any questions?)
References
1. https://resources.narrativescience.com/Resources/Resource-Library/Article-Detail-Page/announcing
-our-new-re...
JAXLondon2017 - Agile Machine Learning: From Theory to Production
JAXLondon2017 - Agile Machine Learning: From Theory to Production
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JAXLondon2017 - Agile Machine Learning: From Theory to Production

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% of enterprises surveyed were already using AI, with 62% 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.

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JAXLondon2017 - Agile Machine Learning: From Theory to Production

  1. 1. From Theory to Production #TeamAwesome Agile Machine Learning
  2. 2. Who are we? Sumanas Sarma @qaoqz Rob Hinds @rob_hinds
  3. 3. Agile Machine LearningAgile Machine Learning
  4. 4. Why should you care?
  5. 5. “Your AI will be a key point of distinction for your business” Accenture - Technology Visions 2017
  6. 6. “Products that don’t use [AI or ML] will die a natural death” Manish Singhal - Forbes India
  7. 7. 62% Percentage of organizations expecting to be using AI Technologies by 2018 Narrative Science - Outlook on Artificial Intelligence in the Enterprise 2016
  8. 8. https://spectrum.ieee.org/computing/software/the-2017-top-programming-languages
  9. 9. https://insights.stackoverflow.com/survey/2017
  10. 10. DON’T BELIEVE THE HYPE
  11. 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. 12. So, what can we do?
  13. 13. Sensible engineering & product design principles are the key
  14. 14. Product Thinking for Machine Learning
  15. 15. https://www.useronboard.com/features-vs-benefits/ Machine Learning != Your Product
  16. 16. Is Machine Learning part of your MVP?
  17. 17. Is your Machine Learning Mission Critical?
  18. 18. https://spectrum.ieee.org/automaton/robotics/artificial-intelligence/how-google-self-driving-car-works
  19. 19. Data (photo) Machine Learning (suggested tags) Curation (creator) Customers (consumers)
  20. 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. 21. Engineering Thinking for Machine Learning
  22. 22. It’s still just Engineering
  23. 23. Clean code Testing Modularity
  24. 24. ML Anti-Patterns: Dead experiment code - Configuration debt Code glue - Pipeline jungles Sculley, D., et al. "Hidden technical debt in machine learning systems."
  25. 25. “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."
  26. 26. Agile Thinking for Machine Learning
  27. 27. Research sprints
  28. 28. Build > Measure > Learn Eric Ries - The Lean Startup
  29. 29. Simple Rule based Traditional off-the-shelf libraries Deep Learning & Sophisticated ML pipelines
  30. 30. Agile Machine LearningAgile Machine Learning
  31. 31. Text ➡ Numbers
  32. 32. 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
  33. 33. 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
  34. 34. Demo
  35. 35. Theory to Production
  36. 36. Choosing your stack Available skills set Existing knowledge & tech stack Hiring pool
  37. 37. Modern architecture & cloud technology makes ML deployment easier
  38. 38. https://pbs.twimg.com/media/C4vf8SQUcAALCyl.jpg
  39. 39. AWS nginx Zuul (Edge server) Eureka (service registry) Recommendation Service Movies Service
  40. 40. Thanks! (any questions?)
  41. 41. 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

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