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WJAX Munich 2017 - 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 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.

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WJAX Munich 2017 - Agile Machine Learning: from Theory to Production

  1. 1. From Theory to Production #TeamAwesome Agile Machine Learning
  2. 2. Who am I? CTO @ Basement Crowd rob_hinds robhinds
  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. “a right to explanation” GDPR: Article 22
  22. 22. Engineering Thinking for Machine Learning
  23. 23. It’s still just Engineering
  24. 24. Clean code Testing Modularity
  25. 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. 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. 27. Agile Thinking for Machine Learning
  28. 28. Research sprints
  29. 29. Build > Measure > Learn Eric Ries - The Lean Startup
  30. 30. Simple Rule based Traditional off-the-shelf libraries Deep Learning & Sophisticated ML pipelines
  31. 31. Agile Machine LearningAgile Machine Learning
  32. 32. Text ➡ Numbers
  33. 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. 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. 35. Demo
  36. 36. Theory to Production
  37. 37. Choosing your stack Available skills set Existing knowledge & tech stack Hiring pool
  38. 38. Modern architecture & cloud technology makes ML deployment easier
  39. 39. Photo by frank mckenna on Unsplash
  40. 40. https://pbs.twimg.com/media/C4vf8SQUcAALCyl.jpg
  41. 41. AWS nginx Zuul (Edge server) Eureka (service registry) Recommendation Service Movies Service
  42. 42. 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
  43. 43. Thanks! (any questions?)
  44. 44. 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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