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Automating Fraud Detection - Continuous Model Deployment
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techsessions
February 14, 2018
Technology
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Automating Fraud Detection - Continuous Model Deployment
Stephen Whitworth, Co-Founder & Machine Learning Engineer, Ravelin
techsessions
February 14, 2018
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Transcript
Stephen Whitworth | 08.02.18 ravelin.com Continuous model deployment
ravelin.com ravelin.com Credit card fraud detection platform for merchants
ravelin.com ravelin.com Score customers in real time for likelihood of
fraud
ravelin.com ravelin.com Machine learning sits at the core of our
detection strategy
ravelin.com ravelin.com Normal ML deployment cycle: release few times a
quarter
ravelin.com ravelin.com Ravelin deployment cycle: deploy new models many times
a week
ravelin.com ravelin.com Frequency reduces difficulty: if something is hard, do
it more often. (Martin Fowler)
Training infrastructure • Python / Go hybrid pipeline • Packaged/distributed
through Docker • On demand compute on big machines • One line to build a new model, run experiments
Pipeline output • New model, trained from scratch • All
output archived to Google Cloud Storage • Performance metrics posted to internal registry • Model deployed to asynchronous live cluster • HTML report of performance for team
Summary report
Comparing two models
• Summarisation over raw details • Minimise manual toil at
all costs • Automation reigns king • Unit test output of models • Make model deployment ‘boring’ Principles for high-performing ML teams
• Data Scientists - join my team! • Head of
Product • Product Managers • Javascript Engineer • Investigations Analyst • Full Stack Engineers • Backend Engineers • Devops Engineer We’re hiring - www.angel.co/ravelin