From Theory to Production

#TeamAwesome

AgileMachine Learning

Who am I?CTO @ Basement Crowd

rob_hinds

robhinds

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 Intelligence in the Enterprise 2016

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

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

DON’TBELIEVE

THE

HYPE

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

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 it2) Do you need ML in your MVP to test product

market fit?3) Is your ML mission critical?

“a right to explanation”GDPR: Article 22

Engineering Thinking

for Machine Learning

It’s still justEngineering

Clean codeTesting

Modularity

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."

Agile Thinking forMachine Learning

Research sprints

Build > Measure > LearnEric 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_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

Demo

Theory to Production

Choosing your stackAvailable skills set

Existing knowledge & tech stack

Hiring pool

Modern architecture & cloud technology

makes ML deployment easier

Photo by frank mckenna on Unsplash

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

AWS

nginxZuul (Edge

server)

Eureka (service registry)

Recommendation Service

Movies Service

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

Thanks!(any questions?)

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