outrun your competition with sas in-memory analytics · 2016. 11. 16. · •automate...
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Copyr i g ht © 2016, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
Outrun Your Competition With SAS® In-Memory AnalyticsSascha SchubertGlobal Technology Practice, SAS
Topics AGENDA
• Challenges with Big Data Analytics
• How SAS can help you to minimize time to value with In-Memory Analytics
• SAS Viya
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Big Data Analytics • Why is it so important now?
Data Computing Power Algorithms
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BUSINESS APPLICATIONS
Big Data Analytics
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““In the new world, it is not the big fish
which eats the small fish, it’s the fast
fish which eats the slow fish.”Klaus Schwab
Founder and Executive Chairman
World Economic Forum
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Lo
st V
alu
e
Data to Decisions Reduce Time to Decision
Producing a new model or
adjusting an existing
model for the business
often takes too long to
meet fast changing
markets.
Complexity is added as
many stakeholders are
involved in the predictive
analytics process.
Big data is adding to the
complexity.
Implementation of a
process model is needed
to provide fast, repeatable
and high-quality results
Value
Time
Data
Latency
Deployment
Latency
Decision
Latency
Lost Time
Modeling
Latency
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Decisions at Scale THE ANALYTIC LIFECYCLE
Regulated
Automated
Governed
Embed
Reliable
Decisions
Consistent
Documented
Actions
IT
Lots of Data
New Data
Experimentation
Fail Fast
Test & Learn
Interactive
Iterative
Innovation
Flexibility
Data Science
Discovery &
Development of
Analytics
Deployment &
Execution of
AnalyticsEXPLORE
PREPARE
MODEL MONITOR
EXECUTE
DEPLOY
ASK
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Factors to Speed up Data to Decisions Time
Support for complete analytical lifecycle
Standardized transparent processes
Minimize data movement for big data volumes
In-memory processing on modern distributed platforms
Easy to use persona-based self service software
Automation of repetitive steps
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Data
SAS/Access® to Big Data • Extract data into SAS
• Push down SQL queries into data
environment
SAS® In-Memory Analytics • SAS native distributed in-memory
computing for fast advanced analytics
• In-memory data exchange
Hig
h S
peed
Hig
h S
peed
Hig
h S
peed
Hig
h S
peed
SAS
Netw
ork
Analytics Server Analytics Server
SQL
SAS® In-Database Technologies• Push SAS processing into data
environment
• Run natively in data environment
Netw
ork
Analytics Server
SAS SAS
Data Data
In-DB Code
Traditional Operational Transformational
Bring SAS Processing to the Data
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SAS® In-Database Technologies
SAS® Scoring Accelerator Aster
DB2
Pivotal
Hadoop
Netezza
Oracle
SAP HANA
SAS® Scalable Performance Data Server
Teradata
SAS® In-Database Code Accelerator Hadoop
Pivotal
Teradata
SAS® Data Quality Accelerator Teradata
Hadoop
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CUSTOMER CASE STUDY
COLLECTIONS MANAGEMENT
DA
TA
EX
PLO
RA
TIO
N
MO
DE
LD
EV
EL
OP
ME
NT
MO
DE
LD
EP
LO
YM
EN
T
• Score all 40 million records compared to the limit of 350 000 in the past
• Reduced Data movement
• Increased data governance
• Better business results: $1M to $3M extra collections a month
Solution Approach : SAS® In-Database Technologies
84SECONDS
40M records
12 min
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SAS® In-Memory Analytics Offerings
Coding
GUI
In-Memory Statistics
High Performance Analytics
Visual Data Mining and Machine
Learning
PROC hpbnet data = creditdata
structure = markovblanket;
model default = x1 LTV income age;
selction = Y
RUN;
In-Memory Statistics
Visual Data Mining
and Machine Learning
Visual Statistics
Enterprise Miner & HPA
Factory Miner
Text Miner & Contextual Analysis
Data Loader for Hadoop
Decision Manager
In-Memory Analytics
Analytics in Action
Us
ab
ilit
y
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SAS In-Memory Machine Learning algorithms are
designed to run on single machine (multi-threaded)
or on a compute cluster
Distributed Data
and Software on
Multiple Servers
Data Scientist
SAS® In-Memory Analytics - Execution
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SAS® In-Memory AnalyticsSINGLE MACHINE VERSUS MASSIVE PARALLEL PROCESSING
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CUSTOMER BEHAVIOR MODELING
Standard Data Mining Process
• Final model is based on a single analytical algorithm – Neuronal Net (NN)
•7 training iterations of the neuronal net take ~5 hours (~1.4 iterations/h.)
•One analyst can generate one model per day
• low productivity
• low confidence
• low model accuracy
•Model lift was 1,6 for top 10%
High-Performance Data Mining
•Final model is based on comparison of several analytical algorithms (NN, SVM, logistic regression,...)
•5000 training iterations of neural net take 70 minutes (~71,4 iterations/min.)
•One analyst can generate many models per day
• High productivity
• High confidence
• High model accuracy
• Model lift improved to 2,5 for top 10%
CUSTOMER CASE STUDY
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SAS® Viya™
• SAS® Viya™ is a new, open
analytic platform built for analytics
innovation
• It is designed for all analytic
professionals, regardless of skills
or experience.
• It scales for data of any size,
speed and complexity.
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SAS® Viya™ SAS® VISUAL DATA MINING AND MACHINE LEARNING
SAS Visual Data Mining and
Machine Learning combines
data wrangling, data
exploration, visualization,
feature engineering, and
modern statistical, data
mining, machine-learning
and text analytics
techniques all in a single,
scalable in-memory
processing environment
– SAS Viya.
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SAS® VISUAL DATA MINING AND MACHINE LEARNING
• K-means and K-modes Clustering
• Principal Component Analysis
• Logistic Regression
• Linear Regression
• Generalized Linear Models
• Nonlinear Regression
• Decision Trees
• Random Forest
• Gradient Boosting
• Neural Networks
• Support Vector Machines
• Factorization Machines
• Network Analytics/Community Detection
• Text Mining
• Boolean Rules
• Autotuning
Data
DeploymentDiscovery
• Assess Supervised
Models
• Complete Score Code
• Multi Threaded Data Step
• DS2
• SQL
• Variable Binning
• Variable Cardinality Analysis
• Sampling and Partitioning
• Missing Value Imputation
• Variable Selection
• Transpose
SAS® Viya™
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SAS® VISUAL DATA MINING AND MACHINE LEARNING
• Hyperparamters• Highly data dependent
• Related to model complexity
• Auto Tuning: • Automate hyperparameters search and find the optimal set
• Maximize predictability on independent data set
• Aims to avoid over-fitting by controlling model complexity
• Creates more accurate models faster vs hand tuning
• SAS auto tuning leverages SAS optimization engines
SAS® Viya™
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SAS STUDIO - WEB-BASED USER INTERFACE
SAS Visual Data Mining and Machine
Learning on SAS Studio
https://youtu.be/X0AU4gDUc_Y
SAS® VISUAL DATA MINING AND MACHINE LEARNINGSAS® Viya™
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OPEN ACCESS TO SAS FROM JUPYTER NOTEBOOK
Other
programming
languages
APIs
SAS language
SAS Visual Data Mining and Machine
Learning with Python Demo
https://youtu.be/LXoikPWQJ3o
SAS® Viya™
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SAS® Viya™SAS® VIYA™ AND SAS® 9
&• It’s an AND strategy
• Can co-exist on same hardware (physical or virtual)
• Data, models, and code can be accessed via bridges
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MORE INFORMATIONSAS® Viya™