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JAX London 2017 - Agile Machine Learning: From Theory to Production

robhinds
October 10, 2017

JAX London 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% 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.

robhinds

October 10, 2017
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  1. “Your AI will be a key point of distinction for

    your business” Accenture - Technology Visions 2017
  2. “Products that don’t use [AI or ML] will die a

    natural death” Manish Singhal - Forbes India
  3. 62% Percentage of organizations expecting to be using AI Technologies

    by 2018 Narrative Science - Outlook on Artificial Intelligence in the Enterprise 2016
  4. “The first wave of corporate AI is doomed to fail”

    Harvard Business Review - The First Wave of Corporate AI Is Doomed to Fail
  5. 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?
  6. ML Anti-Patterns: Dead experiment code - Configuration debt Code glue

    - Pipeline jungles Sculley, D., et al. "Hidden technical debt in machine learning systems."
  7. “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."
  8. 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
  9. 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