building the future of monitoring with artificial intelligence
TRANSCRIPT
Incedo IncBuilding the Future of Monitoring with Artificial
Intelligence
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Our Second Acquisition– increased focus on specialized skills
Launched Incubation Lab for clients
• Specialized Product Engineering Services and Data & Analytics Competency
• Expertise in Financial Services, Life Science and Communication Engineering
• Focused on Emerging Technologies and Innovation - in the context of our clients’ businesses
• Bay Area Headquartered - 1500 employees across North America, South Africa and India (Gurgaon, Bangalore)
• End-to-end capabilities across Application Services, Infrastructure and Operations
• Agile, responsive engagement models
2011 2012 2013 2014 2015 20160
200400600800
1000120014001600
Empl
oyee
s
Incedo Spun out of a diversified $4B Conglomerate
Started US & South Africa Operations
Started Life Sciences and Data & Analytics practice
• 35+ clients• Growth of 75%+ y-o-y
Our First Acquisition - grew Product Engineering Services
Started work with US based Tier 1bank
We Help Businesses Excel And Solve Complex Problems With Technology
Incedo Overview
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Success is proven by growth of more than 671% since its inception in 2011
Incedo in the list of Top 10 Emerging Analytics Start-ups for the year 2016
Our Offices•North America•Santa Clara, New Jersey & New York
•South Africa• Cape Town & Johannesburg•Asia-Pacific (India)• Delhi, Bangalore, Chennai & Mumbai
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We are building a special technology firm and the world has begun to take noteIncedo is an INC 5000 awardee – fastest growing private companies in North America
Incedo recognized by Dataquest for 85% growth
Incedo is one of Top 10 Emerging Analytics Start-ups for the year 2016
Incedo is one of 20 Most Promising Tech Service Providersby CIOReview
Incedo ranks #16 on CRN’s Fast Growth 150
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Feasibility Go-No go
Pilot Project/ Joint Investment
Full Scale Development
What We Do How We Do it
Frameworks
Proof of Concept
Accelerators
Follow Technologies
Track Trends
Rapid Prototyping
3D Visualization Framework on Mobile devices Machine Learning for Edge Devices Master Data Management Solution Accelerators
Accelerators/ Frameworks
IoT Incubation Labs at IncedoWe Partner With Our Clients to Build Smarter Solutions for Them
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AI & NLP Solution – Voice Based BoT Framework
Incubation Labs IoT & Connected Devices AI, NLP, Machine Learning Mobility NFV & Cloud Virtualization
IOT EVOLUTION AND FUTURE TRENDS Incedo’s Perspective of IoT Market
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Internet has come a long way since 1980s but now it has entered into the most exciting phase
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Driven by proliferation of connected devices & sensors
Monitoring and Control
Multi-transport and Multi-function Devices, Sensors
High Speed Wireless – 4G, 5G
Delivering Experience to Consumers
Connectivity and Mobility
Converged, Secured and Collaborative Operations
Digitized
Connected
Services Enabled
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Number of people vs number of connected “things”
By the end of this decade 212 billion “things” are going to be connected to the internet
2003
0.12010
1.82020
30
Consumer60%
Industrial40%
2015
3.5Connected Devices Per Person
We are here, 99.9% of things are still unconnected
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2020 View : Human Centric IOT - “4th Wave, 1.5T+Users/Things”Enabling Man-Machine Collaboration led by Robotics & Artificial Intelligence
Open Innovative eco system
SensorsDevicesMachines
System of
Engagement
System of
Records
Benefit To
Humans
Human Empowerment
Machine Intelligence Connected
Infrastructure
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IoT has many dimensions… and is a Journey… Emerging Industrial IoTIoT Foundation
Disconnected Connect & ControlConnect
Product Smart Product Smart Connected
Product
Connect | Operate | Optimize
Remote Monitoring & Diagnostics
Extend to Ecosystem
Smart Factory – Asset Tracking | Predictive Analytics |
Robotics |Artificial Intelligence
Industrial operations are increasingly connected and monitored with connected devices. Exabytes of data is being generated by these devices, but only a fraction of it is being used in a meaningful way to derive insights and take real-time actions. What is next in industrial evolution?
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Future of Industrial IoT would be Led by AI & Machine LearningiRobot Romba
Sentrian - Disease deterioration modeling platform
Amazon Echo
Artificial Intelligence is turbocharging industries that are generating massive volumes of data through their IoT investments.
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CASE EXAMPLE : COMPUTATIONAL INTELLIGENCE FOR INDUSTRIAL IOT
Incedo’s Experience : Future of Monitoring Led by Artificial Intelligence
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Continuous Monitoring
Safety
Operational Visibility &
KPIs
Predictive Maintenance
Performance &
Optimization
Asset Utilization
Real-time ResponseFast Data : Sub-second
Long Term AnalysisBig Data : Months-Years
Biggest IIoT challenge: deliver business value?
Customers: Early stages of IoT
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Industrial safety solution40+ TB per year
120GB /day of sensor data
Industrial safety solutionBig Data: 40+ TB per year Fast
data: sub-second alerts
Hybrid engine manufacturerBig Data: 720 TB data
Fast Data: 20M Events/Sec
Benefits:• Operational Safety & efficiency • Regulatory compliance.
Benefits• New revenue & services • Predictive maintenance
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IIoT business value is driven by both big & fast data
Safety
Performance &
Optimization
Managing sensor data is key to the value driven by Industrial IOT. Data from 50 billion sensors will generate 35 ZB annual data. 60% of this data is going to be generated by Industrial IOT.
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100’s sensors
Edgegateway
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1000’s sensors
Edgegateway
HighCost
Massive Data
LowBandwidth
Cloud Analytics
FastData
BigData
Ingestion Rate100,000+ data points/second
1000’s of locations
IIoT pain with big and fast data
Programming updatesContinuous learning
Amount of data generated by industrial location is very large and coupled with low BW connectivity presents unique challenges. 20 billion terabytes of industrial sensor data will be generated annually by 2020 * Intel/Cisco
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2,000 Engines 5,512 Sensors
each2 Samples/sec
Incedo’s Solution – Machine Learning & Data Compression
720 TB/Year $1.8M on 4G LTE
Edge Analytics
Not enough Compute
Programmingtakes months
Real time lossless data
40x Compression
Deploy in days
Solution builder tradeoffs - Drop data or face increased cost or forgo- real time analytics
Machine Learning for Edge (Partner Solution)
Sensor Health &Cleansing
Data Accelerator(Data
Compression)Anomaly Detection
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Solution is 2 fold - 1st is crunch the data at edge so that your can enable latency sensitive applications that require real time or near real-time response and 2nd is to enable sending of more data to cloud so you customers can have a global view on analytics and leverage their existing big data investments .
CASE EXAMPLE : NLP FOR SMART OPERATIONS
Incedo’s Experience : Future of Monitoring Led by Artificial Intelligence
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Text, Voice or Personae – Incedo’s Guided Autonomous IntelligenceOverview of Incedo’s BOT Solution Framework
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BOT – Solution which can respond “intelligently” to input (voice and/or text)Intelligence = Input parsing + Context + Knowledge database+ Interactive Output
• Voice/Video to text• Language parsing
o N-gram analysiso Entity Identification
• Building Knowledge Databaseo Supervised Trainingo Unsupervised Training (Machine Learning)
• Context Managemento Domain Specific knowledge
• User Experienceo Responsiveo Richo Interactive
Input to entities
Entities to format ContextDictionary
NLP Engine
Text
Web
Voice
SMS
App
RSS
Management Interface
Domain Unsupervised dataTypical Voice/Chat BoT Application
User Inputs
• Supervised Configuration• Analysis
Incedo has experience in all elements of BoT solutions for Industrial, Healthcare and Communication Engineering
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Personal Chat Assistance for Smart OperationsCase Example : Leading US-Bank required a Robotic Personal Chat Assistant that can be integrated with different internal applications within Banks Framework. The Interactive chat BOT eased the use of reporting platform by addressing common queries and requests of internal employees using existing reporting frameworks in the backend.
Developed solution over Python NLTK with Web, IoS and Mail based interfaces
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Front End
Core EngineReport
Generator Simulator
Look Up
Data
Trained ModelNLTK Model
1
3 4 5
2
Sentence tokenization,pos tagging
Report Dictionary
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Classification
Rest API
Raw Text
User InputEg: Filter & Output type
Augmented workforce for efficient engineering operations
ASR Engine
Service BOTs in Smart Operations which can answer simple speech based questions for routine operations, identify items, search inventories, act as a guide and even summon experts for a video chat
Please confirm the order “Pick up at Bay1, Inventory Lot 5…and Drop point: Bay 2, Loading No.7….
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An example of speech based routine operations for shipment of materials
CASE EXAMPLE : DIAGNOSTICS AS A SERVICE
Incedo’s Experience : Future of Monitoring Led by Artificial Intelligence
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“Smart Car” IoT Service – PoC Overview• Telemetry Data Agent (TDA) uploads car data to the
IoT backend service
• IoT Backend service (AWS IoT) detects abnormal car health conditions (Accidents, Low Battery, Engine & coolant temp, Fuel )
• IoT Backend service notifies Car users and 3rd party service providers about abnormal conditions
• Portal (UI) provides Traffic view, Hazard declaring view, Car service Vehicle analytics view and Hospital Accident/Emergency service views
• Mobile App is used for Registration purpose, View his/her Car health stats, get notified on bad health conditions, get notified if family/friend vehicle has met with accident and details/status of hospital attending. Raise alarm by the owner if need emergency hospital or service need attention
Telemetry Data Agent (TDA)
Car Data
3rd Party Service provider RESTful Applications
Notifications
Mobile App
IoT Backend Service (AWS IoT)
Diagnostics As a Service for Aftermarket Cars Maintenance
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Incedo Automated Testing Framework (ATF) for Industrial IoTOverview
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Incedo Automated Testing Framework (ATF)
SUT Adaptation Engine: Tools/solutions to adapt ATF to SUT solution space
Framework Core (Configuration, Execution, Log Analysis , Reporting Engine; Core Libraries)
Test Scripting Engine (Scripts, Data, Suite)
ReportingEngine
Database
Highlights- Automated testing framework
(Single Click)- Single Node as well as End-2-End
Solution Testing capabilities- Flexibility in adapting to solution
domain- Wide range of reporting,
charting and trend capabilities- Ease of integrating of
opensource/ commercial test solutions into ATF
- XML based scripting- Low/No ramp-up time required
for scripting
Network under Test
Test Tool A Test Tool B
Test Tool CSUT
SUT
SUT
SUT
Case where SUT is a multi-node solution; Test Tools can be tools for injection and/or probe
www.incedoinc.com
Thank You.
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NoteThe content and data shared in the proposal is Incedo proprietary and is intended for the party with whom the presentation is shared. It cannot be shared, copied or redistributed without Incedo’s formal consent.
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