Join us to see how Public-sector organizations and AWS Partners are combining Smart Devices and Artificial Intelligence to create flexible, secure and cost-effective solutions. Applying machine learning models to live video/audio, cameras can be transformed into flexible IoT devices that perform critical functions around public safety, security, property management, smart parking & environmental management. Learn how these solutions are architected using AWS services such as AWS IoT Core, AWS GreenGrass, AWS DeepLens, Amazon SageMaker and Amazon Alexa.

2. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Craig Lawton, IoT Specialist Architect
July 2018
Machine Learning at the Edge
ML, IoT and Edge Compute

3. MACHINE LEARNING at Amazon
Personalized
recommendations
Inventing entirely
new customer
experiences
Fulfillment
automation and
inventory
management
Drones Voice driven
interactions

4. The Machine Learning Process Is Hard …

5. Fetch data
Clean &
format data
Prepare &
transform
data
The Machine Learning Process Is Hard …

6. Fetch data
Clean &
format data
Prepare &
transform
data
Train model
Evaluate
model
The Machine Learning Process Is Hard …

7. Fetch data
Clean &
format data
Prepare &
transform
data
Train model
Evaluate
model
Integration
with prod
Monitor /
debug /
refresh
The Machine Learning Process Is Hard …

8. A fully managed service that enables data scientists and developers to quickly and easily
build machine-learning based models into production smart applications.
Amazon SageMaker

9. Amazon SageMaker
Fully managed
hosting with auto-
scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
training
Hyperparameter
optimisation
BU IL D TR AIN D EPL O Y
DATA!!!

12. https://aws.amazon.com/ml-solutions-lab/

13. I N T HE AW S CLO UD
Application
services
Training InferenceTuning
Machine Learning in the Cloud

14. I N T HE AW S CLO UD
Inference
DE V I C E S & L A M B DA @ E D G E
Application
services
Training InferenceTuning
Machine Learning in the Cloud
And at the EDGE

15. I N T HE AW S CLO UD
Inference
DE V I C E S & L A M B DA @ E D G E
Application
services
Training InferenceTuning
Machine Learning in the Cloud
And at the EDGE

16. Sophisticated models
in the cloud
Language and
speech models
Machine Learning in the Cloud
And at the EDGE
Amazon Echo

17. Sophisticated models
in the cloud
Vision
Models
Machine Learning in the Cloud
And at the EDGE
AWS DeepLens

18. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

19. AWS DeepLens

20. AWS DeepLens Project Components

21. Import Model

22. AWS DeepLens Artifacts
Model
AWS IoT
AWS Greengrass
Device Stream
Project Stream
(optional)
Scene
Device

23. AWS Greengrass Inference Function

24. Get a Frame, Run Inference

25. Inference Output (MQTT)

26. Optimising A Custom Model
Optimises a custom model to CI-DNN format so it can run on the GPU

27. http://benchmark.ini.rub.de/?section=gtsrb
&subsection=dataset
• Single-image, multi-class classification
problem
• More than 40 classes
• More than 50,000 images in total
• Image sizes vary between 15x15 to
250x250 pixels

29. AWS DeepLens Artifacts
Model
AWS IoT
AWS Greengrass
Device Stream
Project Stream
(optional)
Scene
Device

30. AWS DeepLens runs AWS GreenGrass
Edge Cloud
Machine
inference
Inference Training

31. AWS DeepLens runs AWS GreenGrass
Edge Cloud
Machine
inference
Inference Training

32. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Customer Video / Demo
Dynamo6 - Hamilton City Council- TBD

33. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Local AWS Lambda Running on AWS Greengrass
A lambda function
running on a
Greengrass Core
local to the Council
network
preprocesses the raw
video files.
Video is optimised
and chunked before
then being pushed
into AWS Kinesis for
analysis.
AI / ML functions

34. Optimising A Custom Model
Optimises a custom model to CI-DNN format so it can run on the GPU
OpenVINO Toolkit

36. OpenVINO / Intel
• Enables CNN-based deep learning inference on the edge
• Supports heterogeneous execution across computer vision
accelerators—CPU, GPU, Intel® Movidius™ Neural Compute Stick,
and FPGA—using a common API
• Speeds time to market via a library of functions and preoptimized
kernels
• Includes optimized calls for OpenCV and OpenVX
• IEI* Integration Corporation and AAEON developer kits
• https://github.com/intel/Edge-optimized-models
• MobileNet 1, MobileNet 5, SqueezeNet 5 for Pedestrians, Cars,
Buses, Bicycles and Motorcycles

37. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

38. Where do I want to process data?
CloudEdge

39. Where do I want to process data?
Edge Cloud
Law of Economics
Law of Physics
Law of the Land

40. Where do I want to process data?
Edge Cloud
Law of Economics
Law of Physics
Law of the Land

41. Where do I want to process data?
I n f r a s t r u c t u r e C l o u dP o PI o T E n d p o i n t G a t e w a y A p p l i a n c e
C o m m o n P r o g r a m m i n g M o d e l
O n b o a r d
A W S
C l o u d
L a m b d a
@ E d g e
A m a z o n
F r e e R T O S
G r e e n g r a s s

42. A W S
C l o u d
G r e e n g r a s s
Where do I want to process data?
I n f r a s t r u c t u r e C l o u dP o PI o T E n d p o i n t G a t e w a y A p p l i a n c eO n b o a r d
A m a z o n
F r e e R T O S
L a m b d a
@ E d g e

43. AWS Greengrass ML Inference
Voice/sound
recognition
Collision
avoidance
Image
recognition
Anomaly
detection
More
!
Inference TrainingEdge Cloud
Some use
cases
Machine
inference

44. Lots of options…
New Features
Machine Inference
Protocol Adapters
Over the Air Updates
Local Resource Access
Works with Amazon FreeRTOS
Broader Ecosystem
more distributions
preview
coming soon
New Languages
New
New

45. We build IoT solutions through our good friends
AWS Partner Ecosystem
System Integrators
Network
Connectivity
OEM/ ODM
ISVs
Silicon / Chipset / Module
AWS IoT building blocks
Things Cloud
Intelligenc
e Gateways

46. What will you build?
https://docs.aws.amazon.com/deeplens/latest/dg/deeplens-templated-projects-overview.html
https://aws.amazon.com/deeplens/community-projects/

47. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
National Convention Centre, Canberra
11th September – Training, Workshops, GameDay
12th September – Summit day
Keynote featuring Glenn Gore, AWS Head Technologist
Helen Souness, CEO RMIT Online
Denis Bauer, Team Lead Transformational Bioinformatics, CSIRO

48. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
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