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Big Data for Machine
Learning:
How things have changed over the last
decade
Diana Pfeil, @dianam
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Big Data 10 years ago
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via GIPHY
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The invariant principles of big data
engineering
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DRY/Open it up
make it generic
communicate it with excellent documentation
open it up to all
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Back to the deployment process
"machine learning": [Item1, Item2, Item3, ...]
service.getValue()
P13nFileBasedMappingService
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via GIPHY
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Source: http://iquantny.tumblr.com/post/83696310037/meet-the- re-hydrant-that-unfairly-nets-nyc
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Keep It Simple
Try the most simple approach that will work
Modularize: do one thing well
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Avoid relational databases
administrative hassle
complexity
performance
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Just add a server
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Measure Everything
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What has Changed
choice
so much literature
so much open source
open data
accessibility of big data to all
data ethics
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Models for sentencing
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Failure modes for Northpointe recidivism
model
white african-
america
Labeled high risk, did not re-
offend
23% 45%
Labeled low risk, did re-
offend
48% 28%
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Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
Dangerous Model
Territory
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scale:signi cant impact on everyone's lives
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unfair:illegal or unjust factors used in decision-making
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opaque:model is not open or reviewable by those affected
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no feedback loop:model does not course-correct
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via GIPHY
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The future
Technologies will continue to evolve at incredible
speeds
Devops will get easier and cheaper because
serverless!
Machine learning will still require thinking and
domain expertise
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Questions?
@dianam

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Big Data for Machine Learning: How things have changed over the last decade