Introduction to Amazon Go and Amazon Go Tour by Humphrey Chan

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Amazon Rekognition
Humphrey Chen
Senior Manager
Amazon Rekognition

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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

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Amazon Textract Features
Text extraction Table extraction Form extraction

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

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

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Challenges for processing documents
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

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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

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

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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

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

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Amazon Rekognition Features
Faces Celebrities Labels
Moderation ScenesActivities Paths
Text

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

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

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“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.”

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Face – Customer Analysis
With trends, not individually

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Face – Customer Analysis
With trends, not individually

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Face Detection, Analysis, and Recognition
LightingPose Blur and occlusion

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

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