Industrial Machine Learning

Applied Artificial Intelligence in the New Industrial Revolution, 13 April 2017 (SF)

Josh Bloom @profjsb

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

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

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

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

IIoT: Beyond “Smart” Thermostats, Fitbits, and Self-driving cars…

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

Industrial Machine Learning as a Systems Challenge

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

Copyright 2012-2017, wise.io inc.

10

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!

11

>$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

12

“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

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

Prediction API

in-houseas a service

experimental/sandbox

production/scale ready

watsonAPI

Prediction API

in-houseas a service

experimental/sandbox

production/scale ready

watsonAPI

time & cost to

implement cost to maintain

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

Example Industrial Machine Learning Application

Inline Pipeline Inspection

Technology ▶ Action

+

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

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

Industrial Machine Learning

Applied Artificial Intelligence in the New Industrial Revolution, 13 April 2017 (SF)

Josh Bloom @profjsb

Thanks! (and yes, we’re hiring…)