The document discusses machine learning (ML) as a powerful tool driven by big data, advanced algorithms, and substantial computing power, providing an overview of its applications across various industries such as healthcare, retail, and manufacturing. It emphasizes the importance of integrating ML into business processes to generate value, highlighting the need for automation and the operationalization of analytics. The author, Dr. Andreas Becks, offers insights into effective practices for implementing ML in corporate strategies and the necessary steps for building and deploying models.

1. Copyright © SAS Institute Inc. All rights reserved.
MACHINE LEARNING –
THE WHY, WHAT, AND HOW
Dr. Andreas Becks, SAS
@becks_andreas

2. Copyright © SAS Institute Inc. All rights reserved.
Maschine Learning – An Example
Everybody uses machine
learning today – in your
photo app it identifies
faces and persons on
your images.

3. Copyright © SAS Institute Inc. All rights reserved.
Cheap Compute Power
and Parallel Processing
“2.5 Exabytes of data are
produced every day – that’s
90 years of HD videos.”
“90 % of the worlds data
today has been created in the
last 2 years alone.”
March 2015, DN Capital
Availability of Data R&D on Algorithms
Machine Learning – Why is ML so HOT?
Three combining trends: big
data, massive compute
power, and better algorithms.

4. Copyright © SAS Institute Inc. All rights reserved.
Cognitive Computing
Artificial
Intelligence
Machine Learning
Neural Networks
Deep Learning
There is a lot of buzz around
some related terms. Often
confused, are they neither
separted nor the same.

5. Copyright © SAS Institute Inc. All rights reserved.
Supervised
Learning
10 Algorithms Machine Learning Engineers Need to Know
Two major groups of algorithms form a set of machine learning tools.
Source: KDnuggets based on Udacity’s Intro to Machine Learning
Unsupervised
Learning
Naïve Bayes
Classific.
Linear
Regression
Logistic
Regression
Decision
Tree
Support
Vector
MachinesEnsemble
Methods
Unsupervised
Learning
Principal
Component
Analysis
Cluster
Algorithms
Singular
Value
Decompo-
sition
Indepen-
dent
Component
Analysis

6. Copyright © SAS Institute Inc. All rights reserved.
The Principle: Recognizing Activities
Learning dependent patterns of movements
Different sports, different
movements –algorithms
learns characteristic patterns
in sensor data.

7. Copyright © SAS Institute Inc. All rights reserved.
Learning dependent patterns of movements
X-Wrist
X - Ankle
Motion
Trajectories
Raw data by motion
and inertial sensors
X-Wrist
True Activity
Labels
X - Ankle
Training data:
movement in context
Machine Learning
Support Vector Machines
Neural Networks
Learned classification
The Principle: Recognizing Activities
Combination of algorithms clusters the different raw data and connects it to
different activties. Doing that, new and previously unknown incoming data can be
appropriately classified.

8. Copyright © SAS Institute Inc. All rights reserved.
Use Cases for Machine Learning
Manufacturing
• Predictive Maintenance
• Warranty reserve estimation
• Propensity to buy
• Demand forecasting
• Telematics
Healthcare
• Alerts and diagnostics from real-
time patient data
• Risk stratification
• Proactive health management
Retail
• Predictive inventory planning
• Recommendation engines
• Upsell and cross-channel
marketing
• Market segmentation
• ROI and customer value
Travel and Hospitality
• Aircraft scheduling
• Dynamic pricing
• Consumer feedback (social
media analysis)
• Customer complaint resolution
Energy
• Smart grid management
• Power usage analytics
• Energy demand and supply
optimization
• Seismic data analysis
• Carbon emission and trading
Sources: Forbes Magazine, July 2016, Harvard Business Review, February 2016
Financial Services
• Risk analytics and regulation
• Customer segmentation
• Cross-/Up-Sell
• Campaign management
• Credit worthiness evaluation
More than a third of early
movers also saw gains in
bottom-line performance using
machine-reengineering
to slash 15% to 70%
of costs from certain
processes.
Business
processes in
every industry
will be affected.

9. Copyright © SAS Institute Inc. All rights reserved.
Example: Predict Maintenance of Computer Tomographs
Thousands of devices,
Tens of thousands event codes per day,
sensor data
1,000s of predictive models
Challenge: Predict failures of components
5 to 10 days in advance
>70% precision
and <20% false positives
Impact on operative processes
Real applications tend
to become complex.
It‘s not only 1 model /
algorithm!

10. Copyright © SAS Institute Inc. All rights reserved.
Managing the Analytical Life Cycle
Only integration of best models into business processes generates value
Discover Deploy
Prepare
data
Explore
Model
Integrate into
business processes
Execute
Evaluate
Ask
IT, Business Analyst, LoB
Robust
Automation
Actions
Decisions
Operations
Experiments
Data Science
New data
Innovation
Explorative
Data Scientist, LoB
DATA
€
Analytics needs two things: building
best models AND bringing them
into production. Automation
needed due to complexity / scale.

11. Copyright © SAS Institute Inc. All rights reserved.
Summary & Next Steps
Collect & explore data, learn patterns, and automate decisions.
Data Science PLUS lines of
businesses
Only the integration in new
processes will lead to
business value. Everyone
needs to understand the
possibilities of ML.
Machine Learning
improves analytics
Machine learning is an
essential part
of Advanced Analytics –
and every corporate
strategy.
Operationalization requires
discovery and action
Automation and
integration are more
important than algorithms
Machine Learning
This e-book provides a
primer on these innovative
techniques as well as 10 best
practices and a checklist for
machine learning readiness.

12. Copyright © SAS Institute Inc. All rights reserved.
Get in Contact with me!
Dr. Andreas Becks, SAS
https://twitter.com/becks_andreas
https://www.linkedin.com/in/andreas-becks-10998058/