Machine learning is a game-changing technology with vast potential in every industry, yet many teams struggle with how to get started. In this “train the trainer” workshop, we share a sample project you can use with your customers and internal teams to have fun while diving deep on deep learning. You will get hands-on experience using Amazon SageMaker to build and deploy a neural network based on a publicly available dataset of 48,000 bird images. Attendees will also create a custom project for AWS DeepLens that detects birds and triggers species identification. By the end of the workshop, you will have a working end-to-end solution. Prerequisites: hands-on experience with Python, AWS Lambda, Amazon SNS, and Amazon S3 are required to get the most value from the workshop. Attendees are expected to be comfortable with the basics of SageMaker, but prior DeepLens experience is not required. Attendees must bring their own laptop.

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Using Amazon SageMaker and AWS DeepLens
with Teams New to Machine Learning
Mark Roy
Partner Solution Architect
Amazon Web Services
G P S W S 4 0 3

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American
Goldfinch
(1.00)

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Wild Turkey
(0.91)

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Northern
Flicker
(Yellow-
Shafted)
(1.00)

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Agenda
Motivation
What makes computer vision compelling for you, your teams, your customers
Solution architecture
How to build a bird species identifier
Workshop
Lab time - let’s build!

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Related sessions
Tuesday, November 27
AIM306 - Build Custom Models for AWS DeepLens with Amazon SageMaker
4:00 p.m. – 5:00 p.m. | Mirage, Grand Ballroom B
Wednesday, November 28
AIM308 - AWS DeepLens Projects: Tips & Tricks
2:30 PM – 3:30 PM | Starvine 3
Tuesday, November 27
AIM302 - Machine Learning at the Edge
12:15 p.m. – 1:15 p.m. | Venetian, Level 2, Venetian F

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What makes computer vision compelling for you,
your teams, your customers

10. Environment
Farming
Recreation
Retail
Sports
Traffic
Healthcare

11. Environment
Farming
Recreation
Retail
Sports
Traffic
Healthcare

12. Environment
Farming
Recreation
Retail
Sports
Traffic
Healthcare

13. Environment
Farming
Recreation
Retail
Sports
Traffic
Healthcare

14. Environment
Farming
Recreation
Retail
Sports
Traffic
Healthcare

15. Environment
Farming
Recreation
Retail
Sports
Traffic
Healthcare

16. Environment
Farming
Recreation
Retail
Sports
Traffic
Healthcare

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How to build a bird species identifier

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Amazon’s machine learning stack
Platform services
Application services
Frameworks & interfaces
Caffe2 CNTK MXNet PyTorch TensorFlow Torch Keras Gluon
AWS Deep Learning AMIs
Amazon
SageMaker
Amazon
Rekognition
image
Amazon
Transcribe
Amazon
Translate
Amazon
Polly
Amazon
Comprehend
Amazon
Lex
AWS
DeepLens
Education
Amazon
Mechanical Turk
Speech LanguageVision
Amazon
Rekognition
video

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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
One-click
training
Hyperparameter
optimization
BUILD TRAIN DEPLOY

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AWS DeepLens
Deep learning-enabled developer kit
HD video camera
Custom-designed
deep learning
inference engine
Micro-SD
Mini-HDMI
USB
USB
Reset
Audio out
Power
On-board compute optimized for
deep learning
Integrates with Amazon SageMaker
and AWS Lambda
Tutorials, examples, demos,
and pre-built models

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Bird species identifier – High-level flow
This is a
Dark-Eyed
Junco (0.92)
Object
detection
Bird species
AWS
DeepLens
Amazon
SageMaker
Scene

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Runtime architecture
SMS
Amazon
Amazon
Lambda function
Send SMS alert
AWS DeepLens
Object detection
S3 Bucket
Cropped bird images
New object
event trigger
Save cropped image
Identify species
Detect
bird

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NABirds – The bird images dataset
Storks Loons … 21 Top Level
Categories
Dark-eyed Junco (Red-
backed/Gray-headed)
Dark-eyed Junco
New World Sparrows
and Allies
Perching Birds
… Each species has
up to 100 images
Parrots Owls
555 Species at
Lowest Level
48,000+ total
images

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Acknowledgement for the NABirds Dataset
Data provided by the Cornell Lab of
Ornithology, with thanks to photographers
and contributors of crowdsourced data at
AllAboutBirds.org/Labs
This material is based upon work supported
by the National Science Foundation under
Grant No. 1010818

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Lab time – Let’s build!

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Workshop labs
Activity Time
Lab 1 Prepare images for training 15 mins
Lab 2 Train the classification model 15 mins
Lab 3 Host the trained model and identify your first bird! 15 mins
Lab 4 Trigger an inference as new pictures arrive in Amazon S3 15 mins
Lab 5 Use AWS DeepLens to detect birds and trigger classification 25 mins
Lab 6 Configure text notification with identified species (optional) 10 mins

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Taking it forward
• Possibilities for deep learning applications are endless
• Start building soon
• Keep learning and teaching your teams and customers
• Looking forward to hearing your success stories

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Resources
• Slides will be available on slideshare
• AWS Machine Learning Blog
https://aws.amazon.com/blogs/machine-learning/
• fast.ai – Fastest way to learn CNN’s and RNN’s
http://www.fast.ai/
• TWiML – This week in ML & AI - Podcast
http://twimlai/
• CI/CD pipelines for Amazon SageMaker
https://github.com/aws-samples/aws-sagemaker-build

30. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Mark Roy
roymark@amazon.com

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