WJAX Munich 2017 - Agile Machine Learning: from Theory to Production
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Transcript of WJAX Munich 2017 - Agile Machine Learning: from Theory to Production
62%Percentage of organizations expecting to be using AI Technologies by 2018
Narrative Science - Outlook on Artificial Intelligence in the Enterprise 2016
https://spectrum.ieee.org/computing/software/the-2017-top-programming-languages
“The first wave of corporate AI is doomed to fail”Harvard Business Review - The First Wave of Corporate AI Is Doomed to Fail
https://www.useronboard.com/features-vs-benefits/
Machine Learning != Your Product
https://spectrum.ieee.org/automaton/robotics/artificial-intelligence/how-google-self-driving-car-works
3 Principles:1) Don’t build Machine Learning for the sake of it2) Do you need ML in your MVP to test product
market fit?3) Is your ML mission critical?
ML Anti-Patterns:Dead experiment code - Configuration debt
Code glue - Pipeline jungles
Sculley, D., et al. "Hidden technical debt in machine learning systems."
“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."
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
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
Photo by frank mckenna on Unsplash
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
References1. https://resources.narrativescience.com/Resources/Resource-Library/Article-Detail-Page/announcing
-our-new-research-report-outlook-on-artificial-intelligence-in-the-enterprise-20162. https://www.accenture.com/us-en/insight-disruptive-technology-trends-2017 3. http://fortune.com/2016/06/03/tech-ceos-artificial-intelligence4. https://hbr.org/2017/04/the-first-wave-of-corporate-ai-is-doomed-to-fail 5. http://theleanstartup.com/principles6. 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