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C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Artificial Intelligence of Things
Determining the what and how-to for business outcomes
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Agenda
AI / IoT Applications
Key Ingredients
Getting Started
How successful practitioners of AI/IoT are realizing benefits9:00am – 9:30am
Analytics lifecycle, enterprise analytics, and operationalized AI9:30am – 10:45am
Kickstarting AI/IoT initiatives at your organization11:00am – 11:30am
C o m p a n y C o n f id e n t ial – Fo r In t er n a l Use O n lyC o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Transforming a world of data into a world of intelligence
C o m p a n y C o n f id e n t ial – Fo r In t er n a l Use O n lyC o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
C o m p a n y C o n f id e n t ial – Fo r In t er n a l Use O n lyC o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
SAS in a glance
C o m p a n y C o n f id e n t ial – Fo r In t er n a l Use O n lyC o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
6
What is AI?Practical Aspects of AI Possibilities
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Artificial Intelligence (AI) is the science of training computers to perform tasks
that typically require human intelligence to complete.
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Evolution of Artificial Intelligence
Neural Networks1950s-1970s
Machine Learning Deep Learning and Cognitive Systems
1980s-2010s Present Day
SAS
1976
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
What makes AI work?
Modern ML and DL Algorithms• Flexible state-of-the-art performance • Factorization Machines for sparse data• Auto-tuning & automation
Scalable for Big Data• Storage of large tables to fit in memory• Linear scaling with dataset size• Parallelizable using multi-threaded in-
memory technology
Model Deployment & Production• Support for real-time decisioning• Language-agnostic APIs
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Artificial Intelligence at SAS
CoreCapabilities
SupportingTechnologies
Machine Learning Natural Language Forecasting and Optimization
Data Management Visualization Decision Support Deployment
Computer Vision
C o m p a n y C o n f id e n t ial – Fo r In t er n a l Use O n lyC o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
11
AI/IoT ApplicationsHow successful practitioners are realizing benefits
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Performance Assessment
Extract real-time insights during games
Object Detection
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Supply Chain Stability
Improve response to changes in the market
Natural Language Understanding
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Manufacturing Optimization
Identify defects during production
Pattern Recognition
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
ImprovePatient Care
Assemble end-to-end solution for image-
based problems
Image Processing
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Wildlife Conservation
Provide non-invasive monitoring of
endangered species
Image Recognition
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
EnergyForecasting
Use short and long-term variability to improve accuracy
Deep Learning
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Data Management Visualization
Decision Support Deployment
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
AIAI
Computer Vision
Forecasting and Optimization
Machine Learning
Visualization
Data Management
Natural Language
Deployment
Decision Support
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Key IngredientsAnalytics Lifecycle | Enterprise Analytics | Operationalized AI
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Problem /Opportunity
Benefit /Outcome
Profi table
Growth
Data Security
Innovation
Customer Experience
Productivi ty
Attracting/Reta ining Customers
Impact of Fraud
Reputation Management
Increased Share-
holders’ Equity
Risk Reduction
CompetitiveAdvantage
Increased Loyalty
Cost Reduction
Market Share
Risk Management
Brand Equity
Business Value
AutomationData Management
Visualization
Statistics
Machine LearningSelf-Service
Predictive Modeling
Forecasting
Optimization
Analytics Culture
Artificial Intelligence
Advanced AnalyticsPrescriptive Modeling
Exploration
Data Mining
Text Analysis
Collaboration
APIs
How to Frame an Analytics Project
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
How to Frame an Analytics ProjectAnalytics Lifecycle
Data the foundation of everything we do
Discoverythe act of finding something we had not known before
Deploymentwhere we get the value out of analytics
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Problem /Opportunity
Analytics Life Cycle
Benefit /Outcome
Enable with
DATAEnrich through
DISCOVERYEmpower with
DEPLOYMENT
SAS empowers you to
integrate analytical results
and insights back
into your organization with
speed and at scale, from the s imple to the most complex
operating environments.
SAS DIFFERENTIATION
Enrich your data with the widest set of analytical
capabilities -from statistics to machine learning
to cognitive, from SAS to open
source languages for analytics -
with end-to-end support for the entire analytics
life cycle.
Profi tableGrowth
Data Security
Innovation
Customer Experience
Productivi ty
Attracting/Reta ining Customers
Impact of
Fraud
Reputation Management
Increased Share-holders’ Equity
Risk Reduction
Competi tiveAdvantage
Agi le Strategy
Cost Reduction
Market Share
Risk
Management
Brand Equity
Unl ike other data management vendors , SAS
del ivers cleansed,
governed, real-
time data from all your sources
that enables your analytics across
the organization.
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Problem /Opportunity
Analytics Life Cycle
Benefit /Outcome
1 Forrester: 2016 Insights Platforms Accelerate Digital Transformation2 Forrester, Forrester’s 2016 Predictions: Turn Data Into Insight And Action”
POTENTIALFAILURE POINTS
POTENTIALFAILURE POINTS
53%
of data and analytics decision-
makers say it takes too long
to prepare data for
analytics.1
71%
of enterprises don’t think
their firms are effective at connecting
analytics results to business
outcomes.2
Enable with
DATAEnrich through
DISCOVERYEmpower with
DEPLOYMENT
SAS DIFFERENTIATION
Enrich your data with the widest set of analytical
capabilities -from statistics to machine learning
to cognitive, from SAS to open
source languages for analytics -
with end-to-end support for the entire analytics
life cycle.
Unl ike other data management vendors , SAS
del ivers cleansed,
governed, real-
time data from all your sources
that enables your analytics across
the organization.
SAS empowers you to
integrate analytical results
and insights back
into your organization with
speed and at scale, from the s imple to the most complex
operating environments.
Profi tableGrowth
Data Security
Innovation
Customer Experience
Productivi ty
Attracting/Reta ining Customers
Impact of
Fraud
Reputation Management
Increased Share-holders’ Equity
Risk Reduction
Competi tiveAdvantage
Agi le Strategy
Cost Reduction
Market Share
Risk
Management
Brand Equity
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Problem /Opportunity
Benefit /Outcome
THE SAS DIFFERENCE
Analytics Life Cycle
STRATEGIC
WEACCELERATE DATA
ANALYSIS
AND MAXIMIZE THE VALUE OF
YOUR ANALYTICS.
Enable with
DATAEnrich through
DISCOVERYEmpower with
DEPLOYMENT
SAS has automated the deployment,
and can create rea l -time
feedback loops for continuous
optimization.
Profi tableGrowth
Data Security
Innovation
Customer Experience
Productivi ty
Attracting/Reta ining Customers
Impact of
Fraud
Reputation Management
Increased Share-holders’ Equity
Risk Reduction
Competi tiveAdvantage
Agi le Strategy
Cost Reduction
Market Share
Risk
Management
Brand Equity
SAS helps prep data specifically
for analytics, and uses analytics to detect patterns
and rules, to profi le the data,
to discover missing va lues.
Unstructured
Operational
Web
IoT
Hadoop
Machine Learning
Cognitive
Integration with Open Source
Visualization
Aproachable
Analytics
Reports
Models
Decisions at Scale
Automation
In-Memory
In-Database
In-Stream
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
DATA
Unstructured
Operational
Web
IoT
Hadoop
Machine Learning
Cognitive
Integration with Open Source
Visualization
Aproachable
Analytics
Reports
Models
Decisions at Scale
Automation
In-Memory
In-Database
In-Stream
Problem /Opportunity
Benefit /Outcome
With SAS you
can:
Democratize
data across your organization
Get data prepared
quicker
Ensure higher
accuracy
Accelerate time-
to-insight
With SAS you can:
Integrate all your analytics
into your
operations more quickly
Create unmatched
transparency
Create powerful
feedback loops.
Analytics Life Cycle
Profi tableGrowth
Data Security
Innovation
Customer Experience
Productivi ty
Attracting/Reta ining Customers
Impact of
Fraud
Reputation Management
Increased Share-holders’ Equity
Risk Reduction
Competi tiveAdvantage
Agi le Strategy
Cost Reduction
Market Share
Risk
Management
Brand Equity
THE SAS DIFFERENCE
STRATEGIC
Enable with
DATAEnrich through
DISCOVERYEmpower with
DEPLOYMENT
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Streaming AnalyticsEdge | Speed | Analytics
C o m p a n y C o n f id e n t ial – Fo r In t er n a l Use O n lyC o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Analytics LifecycleTraditional Analytics Lifecycle
DeployETL
Data Data Storage
Alerts - Reports Decisioning
Streaming Data
Access - Store - Analyze
C o m p a n y C o n f id e n t ial – Fo r In t er n a l Use O n lyC o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Analytics LifecycleStream – Understand – Act
DeployETL
Data Data Storage
Alerts - Reports Decisioning
Streaming Data Streaming Model Execution
De
plo
y
En
rich
Sto
re
C o m p a n y C o n f id e n t ial – Fo r In t er n a l Use O n lyC o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Why “Edge” Analytics?
Latency Bandwidth Costs
Benefits of Edge Analytics in IoT:
• Latency - in data transfer reduces “time-to-sight” which slows “time-to-action” for responses
• Bandwidth - Using limited bandwidth then prevents other critical uses
• Cost - Sending data incurs IT costs, processing data at the edge reduces costs
Pushing computing applications, data, and services away from centralized nodes to the logical extremes of a network
30
IoT Data
C o m p a n y C o n f id e n t ial – Fo r In t er n a l Use O n lyC o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Information Technology
Devices
Gateway
Operational Data Store
Enterprise Data Warehouse
Edge of Networks
Sensor Readings
Analytics
Sensor
Cloud, On Premise
The IoT Analytics LifecycleEdge Analytics, Distributed Analytics
Operational Technology
Edge By 2019, about 40% of
IoT-data will be stored, processed, analyzed, and acted upon close to, or at the edge of the network.
IDC 2016 IoT Futurescape
C o m p a n y C o n f id e n t ial – Fo r In t er n a l Use O n lyC o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
SAS® Event Stream ProcessingEngineered For Speed
Throughput - how many events per second can be ingested
Latency - the time it takes for an event to be processed through the defined workflow
Millions of events per second throughput
Millisecond-microsecondresponse latency
On standard commodity hardware
Event Streams are high throughput, low latency data flows
SAS Event Stream Processing provides:
Continuous in-memory processing
OS native application
Threaded pool
Clustering
Linear scalability
Fastest ESP on the market
C o m p a n y C o n f id e n t ial – Fo r In t er n a l Use O n lyC o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
High-End Streaming Analytics @ EdgeMulti-Phase, In-Stream Analytics
Live, in-motion event analysis
Open Source IntegrationDeploy Python and C models
In-Stream AnalyticsDeploy models trained on
historical data at rest
Machine LearningIn-stream model training and
scoring
Model SupervisionControl runtime model
deployment
In-Stream Time PatternsTime series detection and
analysis
Text AnalyticsExtract entities, tokenize text, classify and identify sentiment
In-Stream GeofencingReal time location analytics
Event ProcessingCompute, aggregate, filter,
correlate events
Data QualityBusiness rules data quality and
policy definitions
C o m p a n y C o n f id e n t ial – Fo r In t er n a l Use O n lyC o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
SAS® Event Stream ProcessingAdvanced Streaming Analytics
SAS® DS2, Python, CSAS® ASTORE Scoring supportSAS® Model Manager Integration
Streaming Summary - Univariate StatisticsStreaming Pearson’s CorrelationStreaming Segmented CorrelationWeibull Distribution FittingShort Time Fourier TransformStreaming Text TokenizationStreaming Text VectorizationMoving Relative Range
*Out-of-Stream Training & In-Stream Scoring
Support Vector Machines*Streaming Support Vector MachinesStreaming Linear RegressionStreaming Logistic RegressionStreaming Fit StatisticsStreaming Receiver Operating Characteristic (ROC)Streaming HistogramImage Processing(Crop, rotate, resize, flip)Robust Principle Components Analysis*
Streaming Algorithms & Machine Learning
In-Stream Analytic Model Deployment
Streaming K-MeansStreaming DBSCANRandom Forest*Gradient Boosting Tree *Factorization Machine*Support Vector Data Description*Deep Neural Network*Convolutional Neural Network*Bayesian Network*Recurrent Neural Network
C o m p a n y C o n f id e n t ial – Fo r In t er n a l Use O n lyC o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
NetworkInfrastructure
Reference Architecture for IoT using SAS®
Making analytics actionable for IoT
Edge On-Premise or Off-Premise Data Center or Cloud
HadoopSAS
ESP Server
ESP Model
Event Stream Manager
with SAS ESP Edge
Sensors IoT Gateways
ESP Model
updates
ESP Version updates
MQTT
MQTT
EDGE Data Center
Deploy
Develop
EnrichPub/sub
Deploy
Collect
Trigger
C o m p a n y C o n f id e n t ial – Fo r In t er n a l Use O n lyC o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
SAS® Event Stream ProcessingEcosystem Integration - 300+ Endpoints
OPEN SOURCE
SYSTEMS & APPLICATIONS
PUBLISH & SUBSCRIBE API
CONNECT TO ANY SYSTEM WITH JAVA, C++, PYTHONFULLY DOCUMENTED AND EASY TO USE
RendezVous
STANDARDSFILE/SOCKET
XML / JSON
ODBC
JMS
MQTT
OPC-UA
HTTP RESTFUL
WEB SERVICES
WEBSOCKETS
SMTP
NETWORK SNIFFERS
DB LOG SNIFFERS
SYSLOG
UVC
*
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Getting StartedKickstarting AI/IoT initiatives at your organization
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
ModelAccess Explore AnalyzePrepare MonitorCleanse Govern Embed
DISCOVERYDATA DEPLOYMENT
AUTOMATE & ORCHESTRATE
PerformanceTime to Value Personnel Oversight
Considerations Summary
C o m p a n y C o n f id e n t ial – Fo r In t er n a l Use O n lyC o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
SciSports
“Our ambition is to bring real-time data analytics to billions of soccer fans all over the world. By partnering with SAS, we can make
that happen.”
Giels BrouwerFounder and CEO
SciSports
American Honda Motor Co.
“Now, with SAS, it takes less than a minute to identify a suspicious claim. And in that time,
they are finding a noncompliant claim 76 percent of the time.”
Kendrick KauAssistant Manager
Advanced Analytics group, Honda
SAS is working with customers today to make AI an Opportunity
C o m p a n y C o n f id e n t ial – Fo r In t er n a l Use O n lyC o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
C o m p a n y C o n f id e n t ial – Fo r In t er n a l Use O n lyC o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Let us help you get started with AI
“Lack of available skills remains the greatest challenge for CIOs in terms of AI deployment”
Laurence Goasduff, Gartner, Dec, 2017 1
PhD-level experts across multiple AI technologies
Conduct assessments to accelerate your AI innovation opportunities
Access to SAS and Open tools to best fit your unique environment
Focused exclusively on your implementations with direct access to SAS R&D
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
Objective
The AI Journey
Data
People
Process
1
2
3
Technology4
C o p y r ig h t © S AS In st i tu t e In c. A l l r i g h ts r e se r ve d .
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