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1Copyright © 2017, SAS Institute Inc. All rights reserved.
EVOLUTION OF ANALYTICS AND SAS: EMBRACING THE CHANGE AND CHALLENGES
Data Science Talks, Budapest Data Forum, 13 June 2017
Tuba Islam, SAS Global Technology Practice, Analytics
C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
New trends and challenges in the analytics market (15 mins)
A healthcare use case (2 mins)
Demo of new developments at SAS (10 mins)
C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
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Machine Learning is NOT new
Machine Learning
PROC DISCRIM (K-nearest-neighbor discriminant analysis)
– James Goodnight, SAS founder and CEO, 1979
Neural Networks and Statistical Models,
SAS Institute, 1994SAS Data Mining Primer Course
SAS Institute, 1998
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SAS Machine Learning
• Neural networks
• Decision trees
• Random forests
• Associations and sequence discovery
• Gradient boosting and bagging
• Support vector machines
• Nearest-neighbor mapping
• k-means clustering
• Self-organizing maps
• Local search optimization techniques such
as Genetic algorithms
• Regression
• Expectation maximization
• Multivariate adaptive regression splines
• Bayesian networks
• Factorization Machines
• Kernel density estimation
• Principal components analysis
• Singular value decomposition
• Gaussian mixture models
• Sequential covering rule building
• Model Ensembles
• And More…….
ALGORITHMS
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Why Now?
These concepts are not new,
but gaining fresh momentum..
C op yr i g h t © 2016 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
Machine Learning
Artificial Intelligence
Deep Learning
Cognitive Computing
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How Did We Get to “Machine” in Analytics?
The definition has actually been refined over the years
A tool contains one or more parts that uses energy to perform an intended action
Tool
A computer is a type of machine and in that programs are sets of instructions for completing specific actions
Algorithms that learn are now defined as ‘machines’
Computers that have the ability to learn without being explicitly programmed (Arthur Samuel, 1959)
Computer
[Machine] Learning
Algorithm
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1. Traditional Predictive Analysis
Training data
Champion Algorithm
Hypothesis(score code)
Estimated output
Tournament implied
New input
Mo
de
l b
uild
ing
Scoring
• Model building is an “off-line” process
• Scoring is “on-line” or “in-line”
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2. Machine Learning
Training data• This is the
definition of an analytic “machine”!
Hypothesis(score code)
Champion Algorithm
This process repeats until no more improvement is possible (i.e. the model reaches ‘convergence’)
Retrain
Estimated output
New input
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3. The Next-gen of Machine Learning
Training data
Hypothesis(score code)
Champion Algorithm
The technique “learns” from new data
Retrain
Estimated output
New input
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4. True Artificial Intelligence
Training data
Champion 1
(score code)
Decision/Work/ ActionStill a long way off…
…
…
Champion 2 Champion 3 Champion n
Decision logic
Hypothesis 1 Hypothesis 2 Hypothesis 3 Hypothesis n
New input
C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
Input Learning
CustomerTargeting
CropYields
Fraud
Credit Risk
SmartCities
MedicalImages
THE PROCESS OF MACHINE LEARNING IN BUSINESS
C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
APPLICATIONS OF MACHINE LEARNING
Predictive Asset
Maintenance
FraudCredit Scoring
Next Best Offers Customer Segmentation
Targeted Acquisition /
Retention / AttritionReal-time Ad
placements
Natural Language
Processing
Network Intrusion
Detection
Online
Recommendations
Customer Lifetime
Value
C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
MARKET CHALLENGES
Critical decision making
information gets lost in big data
Customers and markets are more
demanding than ever requiring quicker and
more accurate responses
Analytical talent for data driven decision
making can be hard find
C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .Success Story: Geneia Healthcare
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HYBRIDISATION: CHANGE IS COMING..
Embrace Diversity
Hybrid Solutions
Hybrid Data
Hybrid Models
Hybrid Tasks
Hybrid Personas
Accuracy AND interpretability
Use of structured data AND voice/text/
video/images
Proprietary AND open toolsCloud AND on-premise
Data scientists AND power usersTechnology AND business
expertise
Data management machine learning
deployment
C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
SAS POINT OF VIEW
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Benefits of SAS® Viya™ for Machine LearningWhat Does This Mean to You?
• BETTER RESULTS: Improved accuracy
• In-memory analytics platform
• More sophisticated machine learning algorithms
• Auto-tuning functionality
• DIVERSITY: More freedom with openness and collaboration
• Open integration / use of APIs
• Democratisation of data and analytics
• Unified actions available across different interfaces for different personas
• CREATIVITY: New business applications
• Images are now native data sources for SAS!
• Speed in data prep and model build process to cultivate innovation
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Visual Interfaces
Programming Interfaces
API Interfaces
MULTIPLE INTERFACES, SINGLE CODE BASE
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Workers
Controller
proc print data = hmeq (obs = 10);
run;
df = s.CASTable(‘hmeq’)
df.head(10)
df <- defCasTable(s, ‘hmeq’)
head(df, 10)
[table.fetch]
table.name = “hmeq”
from = 1 to = 10
CAS Action
APIs
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3D CHIP PRINTING
1 in a billion failure rate for droplets
50 million droplets per second
Potential for an error every 20 seconds
Classify wafer defects into different categories
Rule based classification was used with 80% accuracy
Semiconductor Manufacturing Industry
Classification which types
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s.image.loadImages(path='/folder/myfolder/img',casOut=vl(caslib='casuser',
name=’ordinary', replace=True),
decode=True)
s.image.loadImages(path='/folder/myfolder/DICOM',casOut=vl(caslib='casuser',
name='medical', replace=True),
recurse=True,series=vl(dicom=True),decode=True)
Medical Imaging IndustryDiagnosis efficiency / Enhanced Research
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SAS Image Processing for SciSportsUse Deep Learning to Recognize Back Numbers
The data is from SciSports and the task is to recognize the numbers athlete's shirts. It contains 6,631 images that have the numbers between 1 and 99. I (aka, XQ) split into train (90%, ~6,000) and test (10%, ~630). The classes are highly
imbalanced. Some numbers only appear less than 10 times.
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developer.sas.com
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SAS Projects on GitHub
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SAS VIYA DEMO
ACCESSING MACHINE LEARNING ACTIONS FROM DIFFERENT INTERFACES
C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
Different PersonasDifferent Interfaces
Same Actions
C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
Data
Discovery Deployment
Relevant and Accurate
SOME THINGSNEVER CHANGE… INGREDIENTS FOR SUCCESS
Domain and Analytics Expertise
Automation and Monitoring
Creativity Collaboration
Communication
C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
Köszönöm!
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