Applied Transportation Analysis Introduction to Intelligent Transportation Systems.
Building Intelligent Data Products (Applied AI)
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Transcript of Building Intelligent Data Products (Applied AI)
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building intelligent data products
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who am i? what does Ravelin do?
building intelligent data products
things to think about when building them
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stephen whitworth
2 years at Hailo as data scientist/jack of some trades out of university
product and marketplace analytics, agent based modelling, data engineering, stream processing
services
data science/engineering at ravelin, specifically focused on our detection capabilities
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what is ravelin?
online fraud detection and prevention platform
stream data to us
we give fraud probability instantly + beautiful data visualisation to understand your customers
backed by techstars/passion/playfair/amadeus/indeed.com founder/wonga founder amongst other great investors
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fraud?
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$14Blost in card not present fraud in 2014
a dollar for every year the universe has existed
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Same day delivery On-demand services
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‘victimless crime’
police ill-equipped to handle
low barrier to entry from dark net
3D secure - conversion killer
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traditional: human generated rules, born of deep expertise
order-centric view of the world
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hybrid: augment expertise by learning rules from data
cards don’t commit fraud, people do
stop the customer before they even get to ordering
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‘a random forest is like a room full of experts who have seen different
cases of fraud from different perspectives’
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‘a random forest is like a room full of experts who have seen different
cases of fraud from different perspectives’
N
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measure and optimise for the right thing(s) in your data product
account for the fact that your customers are at different stages to one another, and optimise for different things
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precision: of all of my predictions, what % was I correct?
recall: out of all of the fraudsters, what % did I catch?
implicit tradeoff between conversion and fraud loss
‘accuracy’ a useless metric for fraud
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99.9% ACCURATE
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use tools that make you disproportionately productive
shameless fans of BigQuery
our analysis stack: BigQuery, JupyterHub, pandas, scikit-learn
internal Google network is super fast, so wise to co-locate with your data
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enable fast iteration by keeping model interfaces simple
hide arbitrarily complex transformations behind it
expose it over REST or a queue
version control them, roll backwards/forwards/sideways
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q: do you always trade performance for explainability? a: no
if someone’s neck is on the line for your decision, allow them to understand how you came to it
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RANDOM FORESTS
MONITORING
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always be monitoring, probing for edge cases
dogfood - use robot customers
run strategies in ‘dark mode’ to determine performance
many ways things could break - be paranoid
‘machine learning: the high interest credit card of technical debt’ - Google
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in beta and signing up clients
looking for on-demand services/marketplaces, payment service providers that are facing fraud
problems
talk to me afterwards
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obligatory: we are hiring!
junior machine learning engineers/data scientists
[email protected] or talk to me after