With IoT, machine learning is going everywhere. Using Amazon SageMaker it's never been easier to build Intelligent Things. In this session we look at how we can push intelligence from cloud-trained models to the edge using AWS Greengrass and explore how devices such as AWS DeepLens make it easy to bring intelligence to your things.

1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Jan Haak, Solutions Architect
Intelligence of Things

2. Agenda
• Intro to AWS DeepLens
• Cover AWS Sagemaker and AWS Greengrass
• Build and Train a Model using AWS Sagemaker
• Deploy Model and Inference code using AWS Greengrass
• Demo!

4. AWS DeepLens
HD video camera
Custom-designed
deep learning
inference engine
Micro-SD
Mini-HDMI
USB
USB
Reset
Audio out
Power
HD video camera
with on-board
compute
optimised for
deep learning
Tutorials,
examples, demos,
and pre-built
models
From
unboxing to
first inference
in <10 minutes
Integrates with
Amazon
SageMaker and
AWS Lambda
10
MIN

5. Sample Projects
Detect and recognise objects.
OBJECT DETECTION
Classify your food.
HOT DOG NOT HOT DOG
Detect a cat or dog.
CAT AND DOG
Transfer a style onto video.
ARTISTIC STYLE TRANSFER
Recognise common activities.
ACTIVITY RECOGNITION
Detect faces of people.
FACE DETECTION

6. But Why Did We Really Build
This? What Is The Problem?

9. Where Will “Compute” Exist In The Future?

11. AWS Greengrass: Local Compute, Messaging &
Data Caching
Local
compute
Local
data caching
Secure
communications
Local
messaging

12. AWS Greengrass: How It Works
Built into
devices at
manufacture
Install the Greengrass
runtime
Lambda functions
on AWS & devices
Manage from
AWS Console
Same programming
model
Local
communication
and orchestration

13. Amazon SageMaker
Build, train, and deploy machine learning models
Collect and
prepare
training
data
Choose and
optimise your
ML algorithm
Set up and
manage
environments
for training
Train and
tune model
(trial and
error)
Deploy model
in production
Scale and
manage the
production
environment

14. 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
BUIL D TRAIN DE PL OY

15. AWS Greengrass: ML Inference
U s e A W S
G r e e n g r a s s
c o n s o l e t o
t r a n s f e r m o d e l s
t o y o u r d e v i c e s
I n f e r e n c e
o n t h e
d e v i c e
D e v i c e s t a k e
a c t i o n
q u i c k l y –
e v e n w h e n
d i s c o n n e c t e d
B u i l d a n d t r a i n
m o d e l s i n t h e
c l o u d

16. Collecting Data

18. Build and train A model
using Amazon SageMaker

19. Build An Amazon SageMaker Model
• Create a Notebook instance
• Import your Data
• Train your model
• Check out reinvent-2017-deeplens-
workshop from github

20. Supported MXNet Layers
Activation BatchNorm Concat Convolution
Deconvolution elemwise_add Flatten FullyConnected
InputLayer Pooling Reshape ScaleShift
SoftmaxActivation SoftmaxOutput Transpose UpSampling
_contrib_MultiBoxDetection fo_contrib_MultiBoxPrior _mul _Plus
LRN L2Norm

21. Deploy Model And Inference Code
Using AWS Greengrass

22. AWS DeepLens

23. AWS DeepLens Project Components

24. Import Model

25. AWS DeepLens Artifacts

26. Greengrass Inference Function

27. [Optional] Create Visual Output Thread

28. Infinite Inference Function – Load Model

29. Get a Frame, Run Inference

30. AWS DeepLens artifacts

31. Inference Output (MQTT)

32. Inference Output (Visual)

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

34. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Demo

35. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. What will you build?

36. Thank you