by Mike Miller, SR. Manager PMT In this session you will get to see AWS DeepLens in action! You will learn how AWS DeepLens empowers developers of all skill levels to get started with deep learning in less than 10 minutes by providing sample projects with practical, hands-on examples which can start running with a single click. In this session you will get an overview of how to build and deploy computer vision models, such as face detection using Amazon SageMaker and AWS DeepLens and learn about some of the great use cases that bring together multiple AWS services to create new to the world deep-learning enabled innovation.

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Mike Miller, Senior Manager, AI Ecosystem
AWS DeepLens:
A New Way to Learn Machine Learning
AWS SF Loft – 3/27/18

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Fulfilment &
Logistics
Search &
Discovery
Existing
Products
New
Products
Thousands of Amazon Engineers Focused on AI

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Artificial Intelligence at Amazon

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AWS DEEPLENS
IS NOT A
VIDEO CAMERA…
…IT’S THE
WORLDS FIRST
DEEP LEARNING
ENABLED
DEVELOPER KIT

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GET STARTED WITH SAMPLE PROJECTS
ARTISTIC STYLE
TRANSFER
OBJECT
DETECTION
FACE DETECTION /
RECOGNITION
HOT DOG / NOT
HOT DOG
CAT VS. DOG
ACTIVITY
DETECTION
ADD CUSTOM FUCTIONALITY
OR
CREATE YOUR OWN PROJECT

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2. DEEPLENS
OVERVIEW
1. MACHINE LEARNING
OVERVIEW
4. EXTENDING A
PROJECT
TODAY WE WILL COVER
3. BUILD & TRAIN
MODELS IN
SAGEMAKER
Amazon
Rekognition
Amazon
S3
AWS
Lambda

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1. MACHINE LEARNING
OVERVIEW

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Model training Inference
OVERVIEW OF DEEP LEARNING
Data

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DATA
Annotate Preprocess Data split

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MODEL DEVELOPMENT & TRAINING
• 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 new data/image just like training set.
2. Feed image back to the trained model to get a predicted output.

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2. DEEPLENS OVERVIEW

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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- AWS DEEPLENS
• Cloud to device
• On the device

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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
I N F E R E N C E
D E P L O Y P R O J E C T S
Manage device
Security
Console Project
Management
AWS Cloud
Intel: Model Optimizer
cIDNN and Driver

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3. BUILD & TRAIN MODELS IN
SAGEMAKER

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
End-to-End
Machine Learning
Platform
Zero Setup Flexible Model
Training
Pay By The
Second
AMAZON SAGEMAKER
The quickest and easiest way to get ML models from idea to production
$

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Amazon SageMaker
Fully managed
hosting with auto-
scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
Hyperparameter
optimization
BUILD TRAIN DEPLOY
One-click
training

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Get Started with Deep Learning in Less
than 10 Minutes with AWS DeepLens
B u i l d c u s t o m d e e p l e a r n i n g m o d e l s i n t h e c l o u d u s i n g
A m a z o n S a g e M a k e r
O R u s e t h e c o l l e c t i o n o f p r e - t r a i n e d m o d e l s i n c l u d e d w i t h
A W S D e e p L e n s

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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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4. Extending a Project:
Audience Response Tracking with AWS

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Amazon
Rekognition
Image
Inference
Lambda
Amazon
S3 Bucket
Amazon
DynamoDB
SageMaker
&
DeepLens
Console
Amazon
S3 Bucket
Recognize
Emotions
Lambda
Training/validation data
Project (Model + Lambda)
Cropped Face Images
Cropped
Face Images
Detected
Emotions
DeepLens
Cloud
Amazon
CloudWatch
Detected
Emotions

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Now, let’s see it in action….

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Thanks & Wrap-Up
Pre-order
aws.amazon.com/deeplens/
Learn more
aws.amazon.com/deeplens/community-projects
Request a workshop
Work with your AWS account
management team to request a
hands-on SageMaker &
DeepLens workshop

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Questions?