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BigML IncBigML, Inc. 1
Machine Learning in the Enterprise
December, 2016Mobey Forum — Toronto
Atakan Cetinsoy V. P., Predictive Applications
[email protected]@atakante
BigML IncBigML, Inc. 2
About BigML
• 33,000+ users across 120 countries
• used in 600+ universities and schools
• promoted by 60+ ambassadors in 20 countries
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Changing of the Guard
SOURCE: Accel Partnershttp://www.businessinsider.com/accel-2017-vc-predictions-2016-11?op=1/#-1
|| $596B
|| $551B
|| $478B
|| $348B
|| $372B
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Algorithmic Modeling
90% of companies 10%(*) of companies
Pure Statistics Machine LearningTraditional Data Modeling
Assume that there is always a statistical model M in the black box.
What’s inside the box is complex and unknown.
Y = M (X, noise, parameters)Y = f algorithm using the input variables to predict the output
variables
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Two Analytics Cultures
SOURCE: Statistical Modeling: The Two Cultures by Leo Breiman. Statistical Science 2001, Vol. 16, No. 3, 199–231
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Two Analytics Cultures
SOURCE: McKinsey & Co.
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Dead Ends
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Source: Doug Laney, Gartner BI Summit
A New Path Forward
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Promise of Machine LearningWant
• Reduce churn • Increase engagement • Improve diagnosis • Reduce fraud etc.
AUTOMATED INSIGHTSDATAHave
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The Platform Advantage
http://www.computing.co.uk/ctg/news/2469593/machine-learning-
can-replace-data-scientists
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ML Platform Traits
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ML in the Wild
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ML in the Wild
SOURCE: https://www.youtube.com/watch?v=jdrFGqwQcCE
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1 Platform, ∞ Predictive AppsSecurity Department Commercial Department Other DepartmentsRisk Department
EstimatingRisk
ExposurePredictive
FraudNext Best
ActionPredictive
HRPredictive Lead
ScoringPredictive
DelinquencyIntrusion Detection
Predictive Maintenance
Customer CRM Product etc. Geo Social Public etc.
EXTERNALINTERNAL
GoogleAWS On-PremisesAzure
• Web UI & Visualizations • REST API & Automated
ML Workflows • Distributed ML
Resources & Algorithms
INFRASTRUCTURE
DATA
ML-BASED DECISION PLATFORM
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The Future AI Stack
SOURCE: http://www.jmlr.org/proceedings/papers/v50/cetinsoy15.pdf
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• Data extraction and analysis
• Personalization and customization
• Continuous experiments
• New kinds of contracts due to better monitoring.
“Taking full advantage of the potential of these new capabilities [enabled by computer mediated transactions] will require increasing sophistication in knowing what to do with the data that are now available.” Hal R. Varian — Chief Economist, Google
The New Frontier
SOURCE: Beyond Big Data, September 2013 http://people.ischool.berkeley.edu/~hal/Papers/2013/BeyondBigDataPaperFINAL.pdf
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Trivial
Medium
BUSINESS RISK
High
Consequential
Decision Automation Matrix
MODEL COMPETENCE
Manual Augment*
AutomateAugment*
★ “As the amount of data goes up, the importance of human judgment should go down.” Andrew McAfee
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Man + Machine for the Win!
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BigML for Alexa
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• ML is the most promising tool to discover and generate immediate value from your business data.
• Cloud ML platforms are maturing fast while removing barriers to entry.
• Competitive pressures will force organizations to train and promote a new class of ML-literate developers, analysts and researchers.
Conclusion
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Atakan CetinsoyVP Smart Applications, BigML
Q&A