Download - Industrial Machine Learning (at GE)
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Industrial Machine Learning
Applied Artificial Intelligence in the New Industrial Revolution, 13 April 2017 (SF)
Josh Bloom @profjsb
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COPYRIGHT 2012-2017, WISE.IO INC.
• Brief Background/Introduction: Me & Wise.io• Industrial Machine Learning (IML) Opportunities• ML as a Systems Engineering Challenge• IML Applications at GE
Agenda
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Teaching
‣ Python Bootcamps 200+ undergrad/grad
‣ Python for Data Science graduate course
Industry
‣ML Applications Company
Code / Repos
Q4’16
CTO, Co-founder Professor, UC Berkeley
Research
Gordon & Betty Moore Foundation
Data-Driven Investigator
‣ Automated Data-driven Discovery & Inference in the Time Domain
‣300+ refereed articles
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COPYRIGHT 2012-2017, WISE.IO INC.
“Intelligent applications in Production”
Customer Support Product ○Intelligent Routing/Triage ○Response Recommendation ○Auto-Response ○Knowledge-base Deflection ○Federated Search ○Spam Filtering ○Sentiment Prediction ○IoT/proactive support
Enhancing Decisions in Human-centric Workflows
• Currently serving dozens of customers in production • Our customers: mid-sized, 5k-5M interactions/month,
charged on a per ticket basis
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COPYRIGHT 2012-2017, WISE.IO INC.
Wise.io @ GE
Build & deploy SaaS-based production-grade scalable intelligent IIoT applications for end business users
Leveraging the data, horizontal edge-to-cloud platform (Predix), & industry relationships already at GE
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IIoT: Beyond “Smart” Thermostats, Fitbits, and Self-driving cars…
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COPYRIGHT 2012-2017, WISE.IO INC.
Consumer Internet Industrial Internet
Data Management Day’s worth of Twitter: 500 GB Single flight: 1 TB
Connectivity Biggest cell phone complaint: dropped calls Mission critical, rough & remote
DeviceSupport
Average wearables lifetime: 6 months
Lifetime of a Turbine: 20+ years
Security Time to Hack most devices: minutes 24/7 Mission Critical
Privacy Privacy is no longer a “social norm” - Zuck HIPAA, ITAR, …
IIoT: The Internet of Really Important Things
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Industrial Machine Learning as a Systems Challenge
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What are we optimizing for?
Component What
Algorithm/Model Learning rate, convexity, error bounds, scaling, …
+ Software/HardwareAccuracy, Memory usage, Disk
usage, CPU needs, time to learn, time to predict
+ Project Stafftime to implement, people/resource costs, reliability,
maintainability, experimentability
+ Consumersdirect value, useability,
explainability, actionability, security, privacy
+ Society indirect value, ethics
- multi-axis optimizations in a given component
- highly coupled optimization considerations between components- myopic view can be costly further up the stack
All ML in production is a Systems Challenge
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Copyright 2012-2017, wise.io inc.
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One ML Algorithmic Trade-OffHigh
LowLow High
Inte
rpre
tabi
lity
Accuracy
Linear/Logistic Regression
Naive Bayes
Decision Trees
SVMs
Bagging
Boosting
Decision Forests
Neural Nets Deep Learning
Nearest Neighbors
Gaussian/Dirichlet
Processes
Splines
* on real-world data setsLasso
Warning
Unscientific &
opinionated!
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>$50k Prize<$50k Prize
Netflix
winning metric
best benchmark
many teams get within ~few % of optimum
so which is easier to put into production?
Leaderboard data from Kaggle & Netflix
Optimization Metric
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“We evaluated some of the new methods offline but the additional accuracy gains that we measured did not seem to justify the engineering effort needed to bring them into a production environment.”
Xavier Amatriain and Justin Basilico (April 2012)
On the Prize
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http://research.google.com/pubs/pub43146.html
• Complex models erode abstraction boundaries
• Data dependencies cost more than code dependencies: weak contracts
• System-level Spaghetti
• Changing External World
“It may be surprising to the academic community to know that only a fraction of the code … is actually doing ‘machine learning’. A mature system might end up being (at most) 5% machine learning code and (at least) 95% glue code.”
see also, Bottou (Facebook) ICML
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Prediction API
in-houseas a service
experimental/sandbox
production/scale ready
watsonAPI
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Prediction API
in-houseas a service
experimental/sandbox
production/scale ready
watsonAPI
time & cost to
implement cost to maintain
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COPYRIGHT 2012-2017, WISE.IO INC.
Wise Architecture: Leveraging Cloud-based ServicesServices Oriented, Leveraging PaaS Managed Services
Microscaling: Dockerized templated workflows for CPU/GPU build/predict end-points
Macro scaling: compute clusters load-balance
RESTful contracts between services
Build on the AWS stack; Instantiated with terraform
End-user Transactional Systems
Embedded UI
Wise App SDK Use Case Specific Middleware
AuthMonitoring/
Alerting
Admin Dashboard
Reporting
Wise Factory
Wise Template (Learn/Prediction/Feedback)
Transaction DB
Model Storage / Management
Fron
t end
Mid
dlew
are
ML
back
end
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Example Industrial Machine Learning Application
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Inline Pipeline Inspection
Technology ▶ Action
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+
seam detected
Crack
Terabytes of Inspection
data
Aggregate historic data
to enable learning from
experience
Advanced machine learning generates
more accurate insights
Surfaced to analysts to improve
performance, drive consistency, & repeatability
Our Goal: drive Zero-Pipeline-Failure
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The Power of a 1% Gain in Efficiency
$27B$30B
$63B$66B
$90B
RailAviation
HealthcarePower
Oil & Gas
Source: “Industrial Internet Pushing Boundaries of Minds & Machines” GE, 2012
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Industrial Machine Learning
Applied Artificial Intelligence in the New Industrial Revolution, 13 April 2017 (SF)
Josh Bloom @profjsb
Thanks! (and yes, we’re hiring…)