This document provides an overview of AWS DeepLens, which is a deep learning enabled developer kit. It describes what AWS DeepLens is, some sample projects that can be created with it including object detection and face recognition, how models are trained and validated, and how inference is performed on the device. It also provides specifications of AWS DeepLens, shows the architecture, and lists some resources for further information.

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Sr. AI Solutions Architect/ ML Solutions Lab / Amazon Web Services
Sunil Mallya
AWS DeepLens
수 네엘 마리아

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AWS DeepLens
is not a
video camera …
It’s the
world’s first
deep learning
enabled
developer kit

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Current Sample Projects
Artistic style
transfer
Object
detection
Face detection /
recognition
Hot dog / not
hot dog
Cat vs. dogActivity
detection
Add custom functionalit y
Or
Create your own project

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Model training Inference
OVERVIEW OF DEEP LEARNING
Data

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Model Training Phase
• Define model architecture
• Input the annotated and cleaned data into the model
• Multiple iterations (epochs) to train the model
• Validate with held back dataset
Large,
annotated
dataset
Training set
Validation set
Training
Validate

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Inference
It’s where the magic happens!
1. Preprocess the new data or image just like a training set.
2. Feed image back to the trained model to get a predicted output.

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

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Create a Project
6. Choose Create.

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Deploy Project to the Device
7. Find your project in
the list (the one you
just named).
8. Choose the radio
button.
9. Choose Deploy to
device.

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10. Select your device.
11. Choose Review.
Target Your Device

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Deploy!
12. Choose Deploy.
A note on costs …

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Wait for the Project to be Deployed
Blue banner = Deployment in progress
Green banner = Deployment successful

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AWS DeepLens Specifications
• Intel Atom Processor
• Gen9 graphics
• Ubuntu OS- 16.04 LTS
• 100 GFLOPS performance
• Dual band Wi-Fi
• 8-GB RAM
• 16-GB storage (eMMC)
• 32-GB SD card
• 4 MP camera with MJPEG
• H.264 encoding at 1080p resolution
• 2 USB ports
• Micro HDMI
• Audio out
• AWS Greengrass preconfigured
• clDNN Optimized for MXNet

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Under the Covers – Console

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Under the Covers – Device

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AWS DeepLens Architecture
Video out
Data out
Inference
Deploy Projects
Manage device
Security
Console Project
Management
AWS Cloud
Intel: Model Optimizer
cIDNN and Driver

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Winners of the DeepLens Hackathon
First place Second place Third place
ReadToMe
Created by Alex Schultz
ReadToMe is a deep learning
enabled application that is
able to read books to kids. In
this case, reading Green Eggs
and Ham, by Dr. Seuss.
Dee
Created by Matthew Clark
Dee is a fun AWS DeepLens
interactive device for children.
The device asks children to
answer questions by showing a
picture of the answer.
SafeHaven
Created by Nathan Stone
and Peter McLean
SafeHaven uses Alexa and
AWS DeepLens to bring
peace of mind for vulnerable
people and their families.
View all 23 projects at: https://aws.amazon.com/deeplens/community-projects

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American Speech Language Translation

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Audience Sentiment Application

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Amazon
Rekognition
image
Inference
Lambda
Amazon
S3 Bucket
Amazon
DynamoDB
Amazon
SageMaker
&
AWS
DeepLens
console
Amazon
S3 Bucket
Recognize
emotions
Lambda
Training/validation data
Cropped face images
Cropped face
images
Detected
emotions
AWS DeepLens
Cloud
Amazon
CloudWatch
Detected
emotions

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Resources
https://github.com/aws-samples/aws-ml-vision-end2end
https://aws.amazon.com/deeplens/
https://aws.amazon.com/blogs/machine-learning/deploy-gluon-models-to-
aws-deeplens-using-a-simple-python-api/

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© 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
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