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Applied Machine Learning:
Beyond the Hype
E360 Annual Conference • Atlanta, Ga. • April 11 and 12
John Wallace Ron Chapek
Director — Innovation, Retail Solutions Director of Product ManagementEmerson Emerson
Applied Machine Learning: Digital Transformation
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The profound and accelerating transformation of business
activities, processes, competencies and models to fully
leverage the changes and opportunities of digital,
data-driven technologies — including the application
of machine learning/artificial intelligence.
3
Applied Machine Learning: What Is Different?
Sensors, switches, actuators, communication protocols,
cloud storage and other core components of IIoT are not new.
What is NEW are lower storage costs, more
intelligence/computing power, ubiquitous networking and
affordable “subscription” access to powerful analytics platforms.
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Applied Machine Learning: New Business Models
These technologies and the resultant (massive) increase in
available data they generate will enable entirely new
value-creation opportunities, business models and
revenue streams.
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Applied Machine Learning: Reality Check
Many companies are in “digital shock” and are struggling to
make the digital culture shift:
• Not started 37%
• Playing catch-up 24%
• On the adoption curve 14%
• Ahead of the adoption curve 25%
Applied Machine Learning: Focus on Value Generation
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Think big but start small, focusing on a specific task with a compelling business (value) proposition.
• Predictive asset management
• Asset life cycle management• Maintenance cost optimization
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How do you plan to implement
AI technology in your
enterprise?
Applied Machine Learning: Question
What Is Machine Learning?
• Machine learning refers to being able to provide a computer with the ability to learn without programming.
• Machine learning is NOT Big Data, IoT, data analytics, dashboards, augmented (or virtual) reality, etc.
• There’s a lot of math, but no “magic”.
• It’s been around for awhile (1959) but recent events (i.e., Cloud processing, high-speed computer processors [CPUs], cost-effective data storage, etc.) have accelerated development and enabled real-world applications.
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https://en.wikipedia.org/wiki/Artificial_intelligence
https://en.wikipedia.org/wiki/Machine_learning
Some Real-World, Everyday Examples; Machine Learning Is All Around Us
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https://www.wired.com/2016/01/the-rise-of-the-artificially-intelligent-hedge-fund/
How do they know what I am searching for?
How do they know what I am saying?
How do they know what to translate?
How do they know where to invest?
https://www.coursera.org/certificate/machine-learning
It’s the machine!
(and a
cloud)
(and connectivity)
thyssenkrupp Utilizing Machine Learning (Predictive Maintenance) to
Drive Optimization in Elevator Maintenance
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https://max.thyssenkrupp-elevator.com/en/
Development championed
by thyssenkrupp
Innovation Center located
at Tech Square
How Does Machine Learning Work?
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It Starts With Inputs and Resulting Actions (Data).
Process or
FunctionInputs
Results
or
actions
The Problem
Given a set of inputs, can we
predict with sufficient accuracy
a result or action taken as a
result of the inputs?
Machine
Learning
Prediction
Model
Inputs
What We Are Trying to Do
Note that Inputs can be anything (i.e., human
language, sensor data, stock market data,
etc.) and Results can be either human
(i.e., translate English to French) or machine
(i.e., predict a failure will occur).
Results
or
actions
Simplified Machine Learning Process;
Creating a Model Is a Data-Intensive, Iterative Process
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Create training
data set(s) and
validation data
set(s) from inputs
and results.
Analyze and
understand what
you are trying
to predict.
Use training data
to evaluate
different models’
performance and
accuracy.
Select initial
model based on
training data
performance.
Input 1 Input 2 Input 3 Result
I11 I21 I31 R1
I12 I22 I32 R2
I13 I23 I33 R3
I14 I24 I34 R4
I15 I25 I35 R5
I16 I26 I36 R6
I17 I27 I37 R7
I18 I28 I38 R8
I19 I29 I39 R9
Input 1 Input 2 Input 3 Result
I11 I21 I31 R1
I12 I22 I32 R2
I13 I23 I33 R3
I14 I24 I34 R4
I15 I25 I35 R5
I16 I26 I36 R6
I17 I27 I37 R7
I18 I28 I38 R8
I19 I29 I39 R9
Input 1 Input 2 Input 3 Result
I11 I21 I31 R1
I12 I22 I32 R2
I13 I23 I33 R3
I14 I24 I34 R4
I15 I25 I35 R5
I16 I26 I36 R6
I17 I27 I37 R7
I18 I28 I38 R8
I19 I29 I39 R9
Utilize new model
to predict results
based on new
inputs.
Use validation
data to check
performance.
1 2 3 4 5 6
A
BA
B
Other Examples: Machine Learning RTU Management (“Overlapping RTU’s”)
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Sales Floor Area RTU’s
RTU’s operating independently
generate demand “peaks”
which impact utility bills.
Supervisory Control App
learns (and predicts) response
of space to RTU state and
coordinates control.
Coordination of RTUs
facilitates comfort and
reduces demand peaks.
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Applying Machine Learning to Refrigeration Systems Data
Cloud-Based
Machine
Learning
Algorithms
Sensors and
Other Data
Operational Insights
Machine Learning Algorithm Predicts Refrigerant Leak
Additional Data Can Predict System Health and Performance.
Dashboard
Delivers
Platforms Are Tools, but Not a Solution; Creating a Solution Requires
Domain Knowledge and a Keen Understanding of the Problem
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Machine Learning Platforms Make the Math Easier, but Still Need Domain Experts as Well as Other Roles to Create a Solution That Drives Value.
The Tool
Domain Expert
The Solution
Microsoft
Azure
IBM
Watson
Amazon
AWS
Data
ScientistsCoders
Domain
Experts
Lots of very
good machine
learning “platforms”
available today
Solution requires the
right platform and a
keen understanding
of the problem
Keys to a Successful Machine Learning Deployment
• Lots of confusion, activity and buzzwords
• Domain knowledge key to understanding the problem to be solved and creating a solution
• Lots of data critical to creating a good model
– Models are only as good as the data used to create them
• Analyze data “inventory” to understand what is available and ensure key data is being collected
• A “platform” is not enough (but can help with the math)
• Start small, but look for something with impact
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Questions?
DISCLAIMER
Although all statements and information contained herein are believed to be accurate and reliable, they are presented without guarantee or warranty of any kind, expressed or
implied. Information provided herein does not relieve the user from the responsibility of carrying out its own tests and experiments, and the user assumes all risks and liability for
use of the information and results obtained. Statements or suggestions concerning the use of materials and processes are made without representation or warranty that any such
use is free of patent infringement and are not recommendations to infringe on any patents. The user should not assume that all toxicity data and safety measures are indicated
herein or that other measures may not be required.
Thank You!
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