NUS-ISS Learning Day 2019-Intelligence on the (bleeding) edge fog bringing ai closer to the customer

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Presented by Ms Lisa Ong, Principal Lecturer & Consultant, Software Systems Practice,NUS-ISS, at NUS-ISS Learning Day 2019

Technology

Intelligence on the (bleeding)
Edge/Fog
Bringing AI Closer to the Customer
#ISSLearningDay
Ms. Lisa Ong, NUS-ISS
2 Aug 2019

Topics
• Edge Computing
• OpenFog Architecture
• AWS DeepLens
• Google Coral
#ISSLearningDay

Edge Computing
“Doing more things nearer to you”
#ISSLearningDay

What is Edge Computing?
• Running Data Processing, Analytics, Algorithms on
intelligent, distributed “device nodes”
instead of on the cloud
• Distributed = placed nearer to data source, sensor, customer
• Fog Computing: when device nodes are connected to internet
#ISSLearningDay

What’s wrong with the cloud?
• Cloud is too far away
• Latency
• Data privacy
• Cloud needs connectivity
infrastructure
• Cloud is expensive to scale
• More devices
• More data streams
#ISSLearningDay The Economist, 2018

Why Edge/Fog Computing?
#ISSLearningDay Source: Industrial IoT Consortium

Powered by 5G
#ISSLearningDay
GSMA Intelligence, 2014

Rising Business Opportunity
#ISSLearningDay
ZDNet, 2018

Projected US18.2bn by 2022
#ISSLearningDay
ZDNet, 2018

Smart Street Lighting Network
#ISSLearningDay
ST Engineering
The Straits Times, 2018

Self-monitoring Plant
#ISSLearningDay
OpenFog Consortium: Bonsai in the Fog

DIY Coffee Vending Machine
#ISSLearningDay
Hackster.io
ESP32 IoT Board

OpenFog Architecture
Guidelines on designing Edge/Fog systems
#ISSLearningDay

OpenFog Consortium
• Established 2015
• Founded by ARM, Cisco, Dell, Intel, Microsoft, Princeton
• Merged into Industrial Internet Consortium in 2019
• Includes AT&T, IBM, General Electric
#ISSLearningDay

Pillars of OpenFog
• Security
• Scalability
• Openness
• Autonomy
• RAS (Reliability, Availability and Serviceability)
• Agility
• Hierarchy
• Programmability
#ISSLearningDay

Hierarchy: N-Tiered Network
#ISSLearningDay
OpenFog Reference Architecture

Autonomy, Scalability
#ISSLearningDay
OpenFog Reference Architecture

Distributed Compute Capabilities
#ISSLearningDay
OpenFog Reference Architecture

Use Case: Airport Journey Tracking
#ISSLearningDay
OpenFog Reference Architecture

#ISSLearningDay
OpenFog Reference Architecture

AWS DeepLens
Video Camera Edge Device with deep learning capabilities
#ISSLearningDay

#ISSLearningDay
https://aws.amazon.com/deeplens/community-projects/

Workflow
#ISSLearningDay
docs.aws.amazon.com/deeplens/latest/dg/how-deeplens-works.html

Google Coral
Efficiently run TensorFlow AI on the Edge
#ISSLearningDay

Deep Learning Accelerators
#ISSLearningDay
spectrum.ieee.org/geek-life/hands-on/the-coral-dev-board-takes-googles-ai-to-the-edge
coral.withgoogle.com/

Benchmarking: Deep Neural Networks
#ISSLearningDay
https://medium.com/@aallan/benchmarking-edge-computing-ce3f13942245

Deployment Workflow
#ISSLearningDay coral.withgoogle.com/docs/edgetpu/models-intro/

Thank You!
lisaong@nus.edu.sg
4-Day Course: NICF - Designing Intelligent Edge Computing (SF)
#ISSLearningDay

Appendix: TF Graph Partitioning
#ISSLearningDay coral.withgoogle.com/docs/edgetpu/models-intro/

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As companies increasingly integrate data across functions, the boundaries between marketing, sales and operations have been blurring. This allows them to find new opportunities that arise by aligning and integrating the activities of supply and demand to improve commercial effectiveness. Instead of conducting post-hoc analyses that allow them to correct future actions, companies generate and analyze data in near real-time and adjust their operations processes dynamically. Transitioning from static analytics outputs to more dynamic contextualized insights means analytics can be delivered with increased relevance closer to the point of decision. This talk will cover the analytics journey from descriptive, predictive and prescriptive analytics to derive actionable and timely insights to improve customer experience to drive marketing, salesforce and operations excellence.

Predictive Analytics

NUS-ISS

With the use of Predictive Analytics, companies are able to predict future trends based on existing available data. The actionable business predictions can help companies achieve cost savings, higher revenue, better resource allocation and efficiency. Predictive analytics has been used in various sectors such as banking & finance, sales & marketing, logistics, retail, healthcare, F&B, etc. for various purposes. Get set to learn more about the different stages of predictive analytics modelling such as data collection & preparation, model development & evaluation metrics, and model deployment considerations will be discussed.

Feature Engineering for IoT

NUS-ISS

In this digital transformation era, we have seen the rise of digital platforms and increased usages of devices particularly in the area of wearables and the Internet of Things (IoT). Given the fast pace change to the IoT landscape and devices, data has become one of the important source of truth for analytics and continuous streaming of data from sensors have also emerged as one of the fuel that revolutionise the emergence of IoT. These includes health telematics, vehicle telematics, predictive maintenance of equipment, manufacturing quality management, consumer behaviour, and more. With this, we will give you an introduction on how to leverage the power of data science and machine learning to understand and explore feature engineering of IoT and sensor data.

Master of Technology in Software Engineering

NUS-ISS

This document provides information about the Master of Technology in Software Engineering program at NUS. The program focuses on designing scalable, smart, and secure software systems and products. It offers both part-time and full-time study structures, with the part-time program taking 2 years and full-time taking 1 year. Students can choose a structured route taking set courses each semester, or a flexible route completing graduate certificates at their own pace over 5-7 years. General admission requirements include a bachelor's degree in engineering or science with a minimum GPA, 2 years of work experience, and passing an entrance test and interview. Important application dates for the 2023 start are also provided.

Master of Technology in Enterprise Business Analytics

NUS-ISS

This document provides information about the Master of Technology in Enterprise Business Analytics program at NUS-ISS. It discusses what data science is, who should take the program, sample job profiles of graduates, the courses taught in the program, and the stackable certificate structure. The program can be completed through a structured route of taking certificates back-to-back over 2 years part-time or 1 year full-time, or a flexible route of taking courses anytime over 7 years to earn the Master of Technology degree. Admission requires a bachelor's degree, minimum GPA, English proficiency, 2 years of work experience, and passing an entrance test and interview.

Diagnosing Complex Problems Using System Archetypes

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In today’s VUCA world, we are faced with problems coming in fast and furious. In order to resolve such problems quickly, we need to first understand the problems. One of the techniques to understand complex problem is through the use of system archetypes. System archetypes are patterns of behaviour of a system. Let’s us explore some of the system archetypes in this session as well as tips on how to resolve them.

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NUS-ISS Learning Day 2019-Intelligence on the (bleeding) edge fog bringing ai closer to the customer

1. Intelligence on the (bleeding) Edge/Fog Bringing AI Closer to the Customer #ISSLearningDay Ms. Lisa Ong, NUS-ISS 2 Aug 2019

2. Topics • Edge Computing • OpenFog Architecture • AWS DeepLens • Google Coral #ISSLearningDay

3. Edge Computing “Doing more things nearer to you” #ISSLearningDay

4. What is Edge Computing? • Running Data Processing, Analytics, Algorithms on intelligent, distributed “device nodes” instead of on the cloud • Distributed = placed nearer to data source, sensor, customer • Fog Computing: when device nodes are connected to internet #ISSLearningDay

5. What’s wrong with the cloud? • Cloud is too far away • Latency • Data privacy • Cloud needs connectivity infrastructure • Cloud is expensive to scale • More devices • More data streams #ISSLearningDay The Economist, 2018

6. Why Edge/Fog Computing? #ISSLearningDay Source: Industrial IoT Consortium

7. Powered by 5G #ISSLearningDay GSMA Intelligence, 2014

8. Rising Business Opportunity #ISSLearningDay ZDNet, 2018

9. Projected US18.2bn by 2022 #ISSLearningDay ZDNet, 2018

10. Smart Street Lighting Network #ISSLearningDay ST Engineering The Straits Times, 2018

11. Self-monitoring Plant #ISSLearningDay OpenFog Consortium: Bonsai in the Fog

12. DIY Coffee Vending Machine #ISSLearningDay Hackster.io ESP32 IoT Board

13. OpenFog Architecture Guidelines on designing Edge/Fog systems #ISSLearningDay

14. OpenFog Consortium • Established 2015 • Founded by ARM, Cisco, Dell, Intel, Microsoft, Princeton • Merged into Industrial Internet Consortium in 2019 • Includes AT&T, IBM, General Electric #ISSLearningDay

15. Pillars of OpenFog • Security • Scalability • Openness • Autonomy • RAS (Reliability, Availability and Serviceability) • Agility • Hierarchy • Programmability #ISSLearningDay

16. #ISSLearningDay OpenFog Consortium

17. Hierarchy: N-Tiered Network #ISSLearningDay OpenFog Reference Architecture

18. Autonomy, Scalability #ISSLearningDay OpenFog Reference Architecture

19. Distributed Compute Capabilities #ISSLearningDay OpenFog Reference Architecture

20. Use Case: Airport Journey Tracking #ISSLearningDay OpenFog Reference Architecture

21. #ISSLearningDay OpenFog Reference Architecture

22. AWS DeepLens Video Camera Edge Device with deep learning capabilities #ISSLearningDay

23. #ISSLearningDay https://aws.amazon.com/deeplens/community-projects/

24. Workflow #ISSLearningDay docs.aws.amazon.com/deeplens/latest/dg/how-deeplens-works.html

25. Google Coral Efficiently run TensorFlow AI on the Edge #ISSLearningDay

26. Deep Learning Accelerators #ISSLearningDay spectrum.ieee.org/geek-life/hands-on/the-coral-dev-board-takes-googles-ai-to-the-edge coral.withgoogle.com/

27. Benchmarking: Deep Neural Networks #ISSLearningDay https://medium.com/@aallan/benchmarking-edge-computing-ce3f13942245

28. Deployment Workflow #ISSLearningDay coral.withgoogle.com/docs/edgetpu/models-intro/

29. Thank You! lisaong@nus.edu.sg 4-Day Course: NICF - Designing Intelligent Edge Computing (SF) #ISSLearningDay

30. Appendix: TF Graph Partitioning #ISSLearningDay coral.withgoogle.com/docs/edgetpu/models-intro/

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