Introduction to Amazon Go and Amazon Go Tour by Humphrey Chan

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Rekognition
Humphrey Chen
Senior Manager
Amazon Rekognition

M L F R A M E W O R K S &
I N F R A S T R U C T U R E
A I S E R V I C E S
R E K O G N I T I O N
I M A G E
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E XR E K O G N I T I O N
V I D E O
Vision Speech Language Chatbots
A M A Z O N
S A G E M A K E R
B U I L D T R A I N
F O R E C A S T
Forecasting
T E X T R A C T P E R S O N A L I Z E
Recommendations
D E P L O Y
Pre-built algorithms & notebooks
Data labeling (G R O U N D T R U T H )
One-click model training & tuning
Optimization (N E O )
One-click deployment & hosting
M L S E R V I C E S
F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e
E C 2 P 3
& P 3 N
E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C
I N F E R E N C E
Reinforcement learningAlgorithms & models ( A W S M A R K E T P L A C E
F O R M A C H I N E L E A R N I N G )
The Amazon ML stack: Broadest & deepest set of capabilities

Amazon Textract Features
Text extraction Table extraction Form extraction

Challenges for processing documents
Optical Character Recognition (OCR)
Output
Extract data quickly & No code templates to
accurately maintain

Amazon Textract –
Text Extraction simplified
Output
Extract data quickly &
accurately
No code or templates to
maintain

Optical Character Recognition (OCR)
Output
Start Date End Date Employer Name Position Held Reason for leaving
1/15/2009 6/30/2013 Any Company Head Baker Family Relocated

Output {
Start Date: 1/15/2009
End Date: 6/30/2013
Employer Name: Any Company
Position Held: Head Baker
Reason for leaving: Family relocated
}
Amazon Textract –
Table Extraction simplified

Output
Full Name Date of Birth Gender
John X Doe 01 01 1971
Male
First Middle Last MM DD YYYY
Female

Output Full Name:
First: John
Middle: X
Last: Doe
Date of Birth:
MM: 01
DD: 01
YYYY: 1971
Gender:
Male: True
Female: False
Amazon Textract –
Form Extraction simplified

Amazon Rekognition:
Deep-Learning-Based Image and Video Analysis

Amazon Rekognition Features
Faces Celebrities Labels
Moderation ScenesActivities Paths
Text

Face – Common Use Cases
Customer Analysis
Analyze trends, not individuals
Recognize Your Customer
Verify the identities of customers who opt-in

Recognize your customer
Verify the identities of customers who opt-in

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
“We developed a ‘Face Ticket’ service using Amazon Rekognition. The
attendees who opted-in have enjoyed the convenience and fun
experience of using our new ‘Face Ticket’ system, and the concerts we
support have much shorter lines.”

Face – Customer Analysis
With trends, not individually

Face Detection, Analysis, and Recognition
LightingPose Blur and occlusion

Private safety
Customer
engagement Movement patterns
…and more
Lower friction Higher efficiency What do you need?
Amazon Rekognition – Use case intersections

Introduction to Amazon Go and Amazon Go Tour by Humphrey Chan

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