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Artificial Intelligence of Things

Determining the what and how-to for business outcomes

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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

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Transforming a world of data into a world of intelligence

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SAS in a glance

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6

What is AI?Practical Aspects of AI Possibilities

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Artificial Intelligence (AI) is the science of training computers to perform tasks

that typically require human intelligence to complete.

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Evolution of Artificial Intelligence

Neural Networks1950s-1970s

Machine Learning Deep Learning and Cognitive Systems

1980s-2010s Present Day

SAS

1976

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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

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Artificial Intelligence at SAS

CoreCapabilities

SupportingTechnologies

Machine Learning Natural Language Forecasting and Optimization

Data Management Visualization Decision Support Deployment

Computer Vision

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11

AI/IoT ApplicationsHow successful practitioners are realizing benefits

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Performance Assessment

Extract real-time insights during games

Object Detection

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Supply Chain Stability

Improve response to changes in the market

Natural Language Understanding

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Manufacturing Optimization

Identify defects during production

Pattern Recognition

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ImprovePatient Care

Assemble end-to-end solution for image-

based problems

Image Processing

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Wildlife Conservation

Provide non-invasive monitoring of

endangered species

Image Recognition

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EnergyForecasting

Use short and long-term variability to improve accuracy

Deep Learning

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Data Management Visualization

Decision Support Deployment

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AIAI

Computer Vision

Forecasting and Optimization

Machine Learning

Visualization

Data Management

Natural Language

Deployment

Decision Support

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Key IngredientsAnalytics Lifecycle | Enterprise Analytics | Operationalized AI

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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

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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

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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.

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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

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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

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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

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Streaming AnalyticsEdge | Speed | Analytics

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Analytics LifecycleTraditional Analytics Lifecycle

DeployETL

Data Data Storage

Alerts - Reports Decisioning

Streaming Data

Access - Store - Analyze

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Analytics LifecycleStream – Understand – Act

DeployETL

Data Data Storage

Alerts - Reports Decisioning

Streaming Data Streaming Model Execution

De

plo

y

En

rich

Sto

re

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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

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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

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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

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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

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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

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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

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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

*

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Getting StartedKickstarting AI/IoT initiatives at your organization

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ModelAccess Explore AnalyzePrepare MonitorCleanse Govern Embed

DISCOVERYDATA DEPLOYMENT

AUTOMATE & ORCHESTRATE

PerformanceTime to Value Personnel Oversight

Considerations Summary

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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

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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

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Objective

The AI Journey

Data

People

Process

1

2

3

Technology4

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